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Choudhury, Tanzeem Khalid 1975. "FaceFacts : study of facial features for understanding expression." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/61109.
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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1999.<br>Includes bibliographical references (p. 79-83).<br>by Tanzeem Khalid Choudhury.<br>S.M.
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Moore, Stephen. "The effects of features and pose on facial expression recognition." Thesis, University of Surrey, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540969.
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Kataoka, Yoshiki. "HER2 expression and its clinicopathological features in resectable gastric cancer." Kyoto University, 2013. http://hdl.handle.net/2433/174804.
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Линдін, Микола Сергійович, Николай Сергеевич Лындин, Mykola Serhiiovych Lyndin, et al. "Peculiar features of CEACAM1 and E-cadherin expression in the uterus." Thesis, Springer, 2018. http://essuir.sumdu.edu.ua/handle/123456789/75152.
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Background & Objective: The aim of our study was to characterize the expression pattern of сarcinoembryonic antigen related cell adhesion molecule 1 (CEACAM1) and E-cadherin in normal, hyperplastic and tumour tissues of the uterine, and to determine the potential correlation between these two adhesion molecules.
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Kehrein, Kirsten. "Organization of mitochondrial gene expression in yeast : Specific features of organellar protein synthesis." Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-107568.
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Mitochondria contain their own genetic system, encoding key subunits of the oxidative phosphorylation system. These subunits are expressed by an organelle-specific gene expression machinery. This work revealed a number of fundamental aspects of mitochondrial gene expression and provides evidence that this process is organized in a unique and organelle-specific manner which likely evolved to optimize protein synthesis and assembly in mitochondria. Most importantly, improving the experimental handling of ribosomes we could show that mitochondrial ribosomes are organized in large assemblies that we termed MIOREX complexes. Ribosomes present in these complexes organize gene expression by recruiting multiple factors required for post-transcriptional steps. In addition, we could reveal mechanisms by which ribosome-interactor complexes modulate and coordinate the expression and assembly of the respiratory chain subunits. For example we showed that the Cbp3-Cbp6 complex binds to the ribosome in proximity to the tunnel exit to coordinate synthesis and assembly of cytochrome b. This location perfectly positions Cbp3-Cbp6 for direct binding to newly synthesized cytochrome b and permits Cbp3-Cbp6 to establish a feedback loop that allows modulation of cytochrome b synthesis in response to assembly efficiency. Likewise the interaction of the membrane-anchor proteins Mba1 and Mdm38 with the tunnel exit region enables them to participate in the translation of the two intron-encoding genes COX1 and COB in addition to their role in membrane insertion. In summary, work presented in this thesis shows that mitochondrial gene expression is a highly organized and regulated process. The concepts and technical innovations will facilitate the elucidation of many additional and important aspects and therefore contribute to the general understanding of how proteins are synthesized in mitochondria.<br><p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.</p>
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Ersotelos, Nikolaos. "Highly automated method for facial expression synthesis." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/4524.
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The synthesis of realistic facial expressions has been an unexplored area for computer graphics scientists. Over the last three decades, several different construction methods have been formulated in order to obtain natural graphic results. Despite these advancements, though, current techniques still require costly resources, heavy user intervention and specific training and outcomes are still not completely realistic. This thesis, therefore, aims to achieve an automated synthesis that will produce realistic facial expressions at a low cost. This thesis, proposes a highly automated approach for achieving a realistic facial expression synthesis, which allows for enhanced performance in speed (3 minutes processing time maximum) and quality with a minimum of user intervention. It will also demonstrate a highly technical and automated method of facial feature detection, by allowing users to obtain their desired facial expression synthesis with minimal physical input. Moreover, it will describe a novel approach to the normalization of the illumination settings values between source and target images, thereby allowing the algorithm to work accurately, even in different lighting conditions. Finally, we will present the results obtained from the proposed techniques, together with our conclusions, at the end of the paper.
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Carballo, Amador Manuel. "Altering the solubility of recombinant proteins through modification of surface features." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/altering-the-solubility-of-recombinant-proteins-through-modification-of-surface-features(a2a7e7d5-3cc5-4f0c-924e-61bb817c0f3e).html.
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Protein solubility plays an important role whether for biophysical and structural studies, or for production and delivery of therapeutic proteins. Poor solubility could lead to protein aggregation, which is an undesired physicochemical mechanism at any stage of recombinant proteins production. To date, more than half of all recombinant therapeutic proteins are produced in mammalian cells, mainly due to the high similarity of the final product to human protein structures. However, poor secretion can occur, due to misfolded proteins or aggregates leading to cellular stress and proteolysis. Another widely-used expression system is E. coli, which can offer a cost-efficient alternative. This system has an important limitation, since proteins tends to form insoluble protein aggregates in the cytoplasm upon heterologous overexpression. Several strategies are being implemented to improved soluble expression, ranging from culture conditions to solubility enhancing tags. However, there is no universal approach or technology that solves protein aggregation. In this thesis two recently published hypotheses from our group have been applied. One stated that soluble expression of proteins was inversely correlated with the size of the largest positively-charged patch on the protein surface. The second hypothesis (of protein solubility), arose from the finding that the relative content of lysine and arginine residues separated E. coli proteins by solubility. Both hypotheses arose from a study of an extensive dataset of experimental solubilities determined for cell-free expression of E. coli proteins. In combination with other widely used strategies, such as lowering expression temperature and inducer concentration, decreasing non-charged (hydrophobic) patches and addition of helical capping for increasing stability, a rational understanding for directed alteration of solubility in a variety of recombinant proteins has been explored. This includes three protein models to test: (i) recombinant human erythropoietin (rHuEPO) (one of the top selling therapeutics) (ii) recombinant 6-Phosphofructo-2-Kinase/fructose-2,6-bisphosphatase (rPFKFB3) (a product for which over-expression has been sought for characterisation and insight into possible cancer therapy) and (iii) a set of three selected E. coli proteins containing high ratios of lysines to arginines: thioredoxin-1 (TRX), cold shock-like protein cspB (cspB), and the histidine-containing phosphocarrier protein (HPr). It was found that single or multiple point mutations (changing amino acids from positive to negative charge or vice versa; or lysines to arginines) verified the predicted effect on rHuEPO, rPFKFB3, TRX, cspB, and HPr solubility (experimentally defined as the distribution between soluble and total fractions) for expression in E. coli. In addition, the redesigned set of rHuEPO transiently expressed in HEK 293-EBNA cells, suggesting that positively-charged patch size may also influence protein secretion. Further application of these computational and experimental approaches could provide a valuable tool in the design and engineering of proteins, with enhanced solubility, stability and secretion.
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Cui, Chen. "Adaptive weighted local textural features for illumination, expression and occlusion invariant face recognition." University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1374782158.
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Loveless, Ian. "Binary Classification With First Phase Feature Selection forGene Expression Survival Data." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555444873531262.
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Xue, Mingliang. "Discriminant feature extraction and selection for person-independent facial expression recognition." Thesis, Curtin University, 2015. http://hdl.handle.net/20.500.11937/2159.
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This thesis is to develop new facial expression recognition techniques based on 2D/3D images or videos, with the purpose to improve the recognition efficiency and accuracy of the current state-of-art. A fully automatic facial expression recognition system is designed, including real-time landmark detection, spatio-temporal feature extraction, hierarchical classification, and most discriminant facial regions identification for expression recognition. In general, the proposed system improved the facial expression recognition state-of-art.
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Ha, Sook Shin. "Dimensionality Reduction, Feature Selection and Visualization of Biological Data." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77169.
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Due to the high dimensionality of most biological data, it is a difficult task to directly analyze, model and visualize the data to gain biological insight. Thus, dimensionality reduction becomes an imperative pre-processing step in analyzing and visualizing high-dimensional biological data. Two major approaches to dimensionality reduction in genomic analysis and biomarker identification studies are: Feature extraction, creating new features by combining existing ones based on a mapping technique; and feature selection, choosing an optimal subset of all features based on an objective function. In this dissertation, we show how our innovative reduction schemes effectively reduce the dimensionality of DNA gene expression data to extract biologically interpretable and relevant features which result in enhancing the biomarker identification process.
To construct biologically interpretable features and facilitate Muscular Dystrophy (MD) subtypes classification, we extract molecular features from MD microarray data by constructing sub-networks using a novel integrative scheme which utilizes protein-protein interaction (PPI) network, functional gene sets information and mRNA profiling data. The workflow includes three major steps: First, by combining PPI network structure and gene-gene co-expression relationship into a new distance metric, we apply affinity propagation clustering (APC) to build gene sub-networks; secondly, we further incorporate functional gene sets knowledge to complement the physical interaction information; finally, based on the constructed sub-network and gene set features, we apply multi-class support vector machine (MSVM) for MD sub-type classification and highlight the biomarkers contributing to the sub-type prediction. The experimental results show that our scheme could construct sub-networks that are more relevant to MD than those constructed by the conventional approach. Furthermore, our integrative strategy substantially improved the prediction accuracy, especially for those ‘hard-to-classify' sub-types.
Conventionally, pathway-based analysis assumes that genes in a pathway equally contribute to a biological function, thus assigning uniform weight to genes. However, this assumption has been proven incorrect and applying uniform weight in the pathway analysis may not be an adequate approach for tasks like molecular classification of diseases, as genes in a functional group may have different differential power. Hence, we propose to use different weights for the pathway analysis which resulted in the development of four weighting schemes. We applied them in two existing pathway analysis methods using both real and simulated gene expression data for pathways. Weighting changes pathway scoring and brings up some new significant pathways, leading to the detection of disease-related genes that are missed under uniform weight.
To help us understand our MD expression data better and derive scientific insight from it, we have explored a suite of visualization tools. Particularly, for selected top performing MD sub-networks, we displayed the network view using Cytoscape; functional annotations using IPA and DAVID functional analysis tools; expression pattern using heat-map and parallel coordinates plot; and MD associated pathways using KEGG pathway diagrams. We also performed weighted MD pathway analysis, and identified overlapping sub-networks across different weight schemes and different MD subtypes using Venn Diagrams, which resulted in the identification of a new sub-network significantly associated with MD. All those graphically displayed data and information helped us understand our MD data and the MD subtypes better, resulting in the identification of several potentially MD associated biomarker pathways and genes.<br>Ph. D.
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Yu, Kaimin. "Towards Realistic Facial Expression Recognition." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9459.
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Automatic facial expression recognition has attracted significant attention over the past decades. Although substantial progress has been achieved for certain scenarios (such as frontal faces in strictly controlled laboratory settings), accurate recognition of facial expression in realistic environments remains unsolved for the most part. The main objective of this thesis is to investigate facial expression recognition in unconstrained environments. As one major problem faced by the literature is the lack of realistic training and testing data, this thesis presents a web search based framework to collect realistic facial expression dataset from the Web. By adopting an active learning based method to remove noisy images from text based image search results, the proposed approach minimizes the human efforts during the dataset construction and maximizes the scalability for future research. Various novel facial expression features are then proposed to address the challenges imposed by the newly collected dataset. Finally, a spectral embedding based feature fusion framework is presented to combine the proposed facial expression features to form a more descriptive representation. This thesis also systematically investigates how the number of frames of a facial expression sequence can affect the performance of facial expression recognition algorithms, since facial expression sequences may be captured under different frame rates in realistic scenarios. A facial expression keyframe selection method is proposed based on keypoint based frame representation. Comprehensive experiments have been performed to demonstrate the effectiveness of the presented methods.
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Nilsson, Roland. "Statistical Feature Selection : With Applications in Life Science." Doctoral thesis, Linköping : Department of Physcis, Chemistry and Biology, Linköping University, 2007. http://www.bibl.liu.se/liupubl/disp/disp2007/tek1090s.pdf.
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Линдін, Микола Сергійович, Николай Сергеевич Лындин, Mykola Serhiiovych Lyndin, et al. "The features of p53 and Ki-67 expression during Gleason's grade increase in prostate cancer." Thesis, Springer, 2019. http://essuir.sumdu.edu.ua/handle/123456789/75116.
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Background & Objectives: Prostate cancer is the most common malignant tumour of the male reproductive organs. Its course depends on the histological and immunohistochemical characteristics of neoplasias. In view of this, the purpose of study was to investigate the influences of prostate cancer differentiation on peculiarities of Ki-67 and p53 expression.
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Kabir, Mitra. "Prediction of mammalian essential genes based on sequence and functional features." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/prediction-of-mammalian-essential-genes-based-on-sequence-and-functional-features(cf8eeed5-c2b3-47c3-9a8f-2cc290c90d56).html.
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Essential genes are those whose presence is imperative for an organism's survival, whereas the functions of non-essential genes may be useful but not critical. Abnormal functionality of essential genes may lead to defects or death at an early stage of life. Knowledge of essential genes is therefore key to understanding development, maintenance of major cellular processes and tissue-specific functions that are crucial for life. Existing experimental techniques for identifying essential genes are accurate, but most of them are time consuming and expensive. Predicting essential genes using computational methods, therefore, would be of great value as they circumvent experimental constraints. Our research is based on the hypothesis that mammalian essential (lethal) and non-essential (viable) genes are distinguishable by various properties. We examined a wide range of features of Mus musculus genes, including sequence, protein-protein interactions, gene expression and function, and found 75 features that were statistically discriminative between lethal and viable genes. These features were used as inputs to create a novel machine learning classifier, allowing the prediction of a mouse gene as lethal or viable with the cross-validation and blind test accuracies of ∼91% and ∼93%, respectively. The prediction results are promising, indicating that our classifier is an effective mammalian essential gene prediction method. We further developed the mouse gene essentiality study by analysing the association between essentiality and gene duplication. Mouse genes were labelled as singletons or duplicates, and their expression patterns over 13 developmental stages were examined. We found that lethal genes originating from duplicates are considerably lower in proportion than singletons. At all developmental stages a significantly higher proportion of singletons and lethal genes are expressed than duplicates and viable genes. Lethal genes were also found to be more ancient than viable genes. In addition, we observed that duplicate pairs with similar patterns of developmental co-expression are more likely to be viable; lethal gene duplicate pairs do not have such a trend. Overall, these results suggest that duplicate genes in mouse are less likely to be essential than singletons. Finally, we investigated the evolutionary age of mouse genes across development to see if the morphological hourglass pattern exists in the mouse. We found that in mouse embryos, genes expressed in early and late stages are evolutionarily younger than those expressed in mid-embryogenesis, thus yielding an hourglass pattern. However, the oldest genes are not expressed at the phylotypic stage stated in prior studies, but instead at an earlier time point - the egg cylinder stage. These results question the application of the hourglass model to mouse development.
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de, la Cruz Nathan. "Autonomous facial expression recognition using the facial action coding system." University of the Western Cape, 2016. http://hdl.handle.net/11394/5121.
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>Magister Scientiae - MSc<br>The South African Sign Language research group at the University of the Western Cape is in the process of creating a fully-edged machine translation system to automatically translate between South African Sign Language and English. A major component of the system is the ability to accurately recognise facial expressions, which are used to convey emphasis, tone and mood within South African Sign Language sentences. Traditionally, facial expression recognition research has taken one of two paths: either recognising whole facial expressions of which there are six i.e. anger, disgust, fear, happiness, sadness, surprise, as well as the neutral expression; or recognising the fundamental components of facial expressions as defined by the Facial Action Coding System in the form of Action Units. Action Units are directly related to the motion of specific muscles in the face, combinations of which are used to form any facial expression. This research investigates enhanced recognition of whole facial expressions by means of a hybrid approach that combines traditional whole facial expression recognition with Action Unit recognition to achieve an enhanced classification approach.
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Zhang, Ligang. "Towards spontaneous facial expression recognition in real-world video." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/53199/1/Ligang_Zhang_Thesis.pdf.
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Facial expression is an important channel of human social communication. Facial expression recognition (FER) aims to perceive and understand emotional states of humans based on information in the face. Building robust and high performance FER systems that can work in real-world video is still a challenging task, due to the various unpredictable facial variations and complicated exterior environmental conditions, as well as the difficulty of choosing a suitable type of feature descriptor for extracting discriminative facial information. Facial variations caused by factors such as pose, age, gender, race and occlusion, can exert profound influence on the robustness, while a suitable feature descriptor largely determines the performance.
Most present attention on FER has been paid to addressing variations in pose and illumination. No approach has been reported on handling face localization errors and relatively few on overcoming facial occlusions, although the significant impact of these two variations on the performance has been proved and highlighted in many previous studies. Many texture and geometric features have been previously proposed for FER. However, few comparison studies have been conducted to explore the performance differences between different features and examine the performance improvement arisen from fusion of texture and geometry, especially on data with spontaneous emotions. The majority of existing approaches are evaluated on databases with posed or induced facial expressions collected in laboratory environments, whereas little attention has been paid on recognizing naturalistic facial expressions on real-world data.
This thesis investigates techniques for building robust and high performance FER systems based on a number of established feature sets. It comprises of contributions towards three main objectives: (1) Robustness to face localization errors and facial occlusions. An approach is proposed to handle face localization errors and facial occlusions using Gabor based templates. Template extraction algorithms are designed to collect a pool of local template features and template matching is then performed to covert these templates into distances, which are robust to localization errors and occlusions.
(2) Improvement of performance through feature comparison, selection and fusion. A comparative framework is presented to compare the performance between different features and different feature selection algorithms, and examine the performance improvement arising from fusion of texture and geometry. The framework is evaluated for both discrete and dimensional expression recognition on spontaneous data.
(3) Evaluation of performance in the context of real-world applications. A system is selected and applied into discriminating posed versus spontaneous expressions and recognizing naturalistic facial expressions. A database is collected from real-world recordings and is used to explore feature differences between standard database images and real-world images, as well as between real-world images and real-world video frames.
The performance evaluations are based on the JAFFE, CK, Feedtum, NVIE, Semaine and self-collected QUT databases. The results demonstrate high robustness of the proposed approach to the simulated localization errors and occlusions. Texture and geometry have different contributions to the performance of discrete and dimensional expression recognition, as well as posed versus spontaneous emotion discrimination. These investigations provide useful insights into enhancing robustness and achieving high performance of FER systems, and putting them into real-world applications.
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Mistry, Kamlesh. "Intelligent facial expression recognition with unsupervised facial point detection and evolutionary feature optimization." Thesis, Northumbria University, 2016. http://nrl.northumbria.ac.uk/36011/.
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Facial expression is one of the effective channels to convey emotions and feelings. Many shape-based, appearance-based or hybrid methods for automatic facial expression recognition have been proposed. However, it is still a challenging task to identify emotions from facial images with scaling differences, pose variations, and occlusions. In addition, it is also difficult to identify significant discriminating facial features that could represent the characteristic of each expression because of the subtlety and variability of facial expressions. In order to deal with the above challenges, this research proposes two novel approaches: unsupervised facial point detection and texture-based facial expression recognition with feature optimisation. First of all, unsupervised automatic facial point detection integrated with regression-based intensity estimation for facial Action Units (AUs) and emotion clustering is proposed to deal with challenges such as scaling differences, pose variations, and occlusions. The proposed facial point detector can detect 54 facial points in images of faces with occlusions, pose variations and scaling differences. We conduct AU intensity estimation respectively using support vector regression and neural networks for 18 selected AUs. FCM is also subsequently employed to recognise seven basic emotions as well as neutral expressions. It also shows great potential to deal with compound and newly arrived novel emotion class detection. The second proposed system focuses on a texture-based approach for facial expression recognition by proposing a novel variant of the local binary pattern for discriminative feature extraction and Particle Swarm Optimization (PSO)-based feature optimisation. Multiple classifiers are applied for recognising seven facial expressions. Finally, evaluations are conducted to show the efficiency of the above two proposed systems. Evaluated using well-known facial databases: Helen, labelled faces in the wild, PUT, and CK+ the proposed unsupervised facial point detector outperforms other supervised landmark detection models dramatically and shows excellent robustness and capability in dealing with rotations, occlusions and illumination changes. Moreover, a comprehensive evaluation is also conducted for the proposed texture-based facial expression recognition with mGA-embedded PSO feature optimisation. Evaluated using the CK+ and MMI benchmark databases, the experimental results indicate that it outperforms other state-of-the-art metaheuristic search methods and facial emotion recognition research reported in the literature by a significant margin.
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Bezerra, Giuliana Silva. "A framework for investigating the use of face features to identify spontaneous emotions." Universidade Federal do Rio Grande do Norte, 2014. http://repositorio.ufrn.br/handle/123456789/19595.
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Previous issue date: 2014-12-12<br>Emotion-based analysis has raised a lot of interest, particularly in areas such as forensics, medicine, music, psychology, and human-machine interface. Following this trend, the use of facial analysis (either automatic or human-based) is the most common subject to be investigated once this type of data can easily be collected and is well accepted in the literature as a metric for inference of emotional states. Despite this popularity, due to several constraints found in real world scenarios (e.g. lightning, complex backgrounds, facial hair and so on), automatically obtaining affective information from face accurately is a very challenging accomplishment. This work presents a framework which aims to analyse emotional experiences through naturally generated facial expressions. Our main contribution is a new 4-dimensional model to describe emotional experiences in terms of appraisal, facial expressions, mood, and subjective experiences. In addition, we present an experiment using a new protocol proposed to obtain spontaneous emotional reactions. The results have suggested that the initial emotional state described by the participants of the experiment was different from that described after the exposure to the eliciting stimulus, thus showing that the used stimuli were capable of inducing the expected emotional states in most individuals. Moreover, our results pointed out that spontaneous facial reactions to emotions are very different from those in prototypic expressions due to the lack of expressiveness in the latter.<br>Emotion-based analysis has raised a lot of interest, particularly in areas such as forensics, medicine, music, psychology, and human-machine interface. Following this trend, the use of facial analysis (either automatic or human-based) is the most common subject to be investigated once this type of data can easily be collected and is well accepted in the literature as a metric for inference of emotional states. Despite this popularity, due to several constraints found in real world scenarios (e.g. lightning, complex backgrounds, facial hair and so on), automatically obtaining affective information from face accurately is a very challenging accomplishment. This work presents a framework which aims to analyse emotional experiences through naturally generated facial expressions. Our main contribution is a new 4-dimensional model to describe emotional experiences in terms of appraisal, facial expressions, mood, and subjective experiences. In addition, we present an experiment using a new protocol proposed to obtain spontaneous emotional reactions. The results have suggested that the initial emotional state described by the participants of the experiment was different from that described after the exposure to the eliciting stimulus, thus showing that the used stimuli were capable of inducing the expected emotional states in most individuals. Moreover, our results pointed out that spontaneous facial reactions to emotions are very different from those in prototypic expressions due to the lack of expressiveness in the latter.
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Esterhuysen, Fanechka Naomi. "Development of a simple artificial intelligence method to accurately subtype breast cancers based on gene expression barcodes." University of the Western Cape, 2018. http://hdl.handle.net/11394/6768.
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>Magister Scientiae - MSc<br>INTRODUCTION:
Breast cancer is a highly heterogeneous disease. The complexity of achieving an accurate diagnosis and an effective treatment regimen lies within this heterogeneity. Subtypes of the disease are not simply molecular, i.e. hormone receptor over-expression or absence, but the tumour itself is heterogeneous in terms of tissue of origin, metastases, and histopathological variability. Accurate tumour classification vastly improves treatment decisions, patient outcomes and 5-year survival rates. Gene expression studies aided by transcriptomic technologies such as microarrays and next-generation sequencing (e.g. RNA-Sequencing) have aided oncology researcher and clinician understanding of the complex molecular portraits of malignant breast tumours. Mechanisms governing cancers, which include tumorigenesis, gene fusions, gene over-expression and suppression, cellular process and pathway involvementinvolvement, have been elucidated through comprehensive analyses of the cancer transcriptome. Over the past 20 years, gene expression signatures, discovered with both microarray and RNA-Seq have reached clinical and commercial application through the development of tests such as Mammaprint®, OncotypeDX®, and FoundationOne® CDx, all which focus on chemotherapy sensitivity, prediction of cancer recurrence, and tumour mutational level.
The Gene Expression Barcode (GExB) algorithm was developed to allow for easy interpretation and integration of microarray data through data normalization with frozen RMA (fRMA) preprocessing and conversion of relative gene expression to a sequence of 1's and 0's. Unfortunately, the algorithm has not yet been developed for RNA-Seq data. However, implementation of the GExB with feature-selection would contribute to a machine-learning based robust breast cancer and subtype classifier.
METHODOLOGY:
For microarray data, we applied the GExB algorithm to generate barcodes for normal breast and breast tumour samples. A two-class classifier for malignancy was developed through feature-selection on barcoded samples by selecting for genes with 85% stable absence or presence within a tissue type, and differentially stable between tissues. A multi-class feature-selection method was employed to identify genes with variable expression in one subtype, but 80% stable absence or presence in all other subtypes, i.e. 80% in n-1 subtypes.
For RNA-Seq data, a barcoding method needed to be developed which could mimic the GExB algorithm for microarray data. A z-score-to-barcode method was implemented and differential gene expression analysis with selection of the top 100 genes as informative features for classification purposes.
The accuracy and discriminatory capability of both microarray-based gene signatures and the RNA-Seq-based gene signatures was assessed through unsupervised and supervised machine-learning algorithms, i.e., K-means and Hierarchical clustering, as well as binary and multi-class Support Vector Machine (SVM) implementations.
RESULTS:
The GExB-FS method for microarray data yielded an 85-probe and 346-probe informative set for two-class and multi-class classifiers, respectively. The two-class classifier predicted samples as either normal or malignant with 100% accuracy and the multi-class classifier predicted molecular subtype with 96.5% accuracy with SVM.
Combining RNA-Seq DE analysis for feature-selection with the z-score-to-barcode method, resulted in a two-class classifier for malignancy, and a multi-class classifier for normal-from-healthy, normal-adjacent-tumour (from cancer patients), and breast tumour samples with 100% accuracy. Most notably, a normal-adjacent-tumour gene expression signature emerged, which differentiated it from normal breast tissues in healthy individuals.
CONCLUSION: A potentially novel method for microarray and RNA-Seq data transformation, feature selection and classifier development was established. The universal application of the microarray signatures and validity of the z-score-to-barcode method was proven with 95% accurate classification of RNA-Seq barcoded samples with a microarray discovered gene expression signature. The results from this comprehensive study into the discovery of robust gene expression signatures holds immense potential for further R&F towards implementation at the clinical endpoint, and translation to simpler and cost-effective laboratory methods such as qtPCR-based tests.
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Moles, Lopez Xavier. "Characterization and Colocalization of Tissue-Based Biomarker Expression by Quantitative Image Analysis: Development and Extraction of Novel Features." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209330.
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Proteins are the actual actors in the (normal or disrupted) physiological processes and immunohistochemistry (IHC) is a very efficient mean of visualizing and locating protein expression in tissue samples. By comparing pathologic and normal tissue, IHC is thus able to evidence protein expression alterations. This is the reason why IHC plays a grow- ing role to evidence tissue-based biomarkers in clinical pathology for diagnosing var- ious diseases and directing personalized therapy. Therefore, IHC biomarker evaluation significantly impacts the adequacy of the therapeutic choices for patients with serious pathologies, such as cancer. However, this evaluation may be time-consuming and dif- ficult to apply in practice due to the absence of precise positive cut-off values as well as staining (i.e. protein expression) heterogeneity intra- and inter-samples. Quantifying IHC staining patterns has thus become a crucial need in histopathology. For this task, automated image analysis has multiple advantages, such as avoiding the evidenced ef- fects of human subjectivity. The recent introduction of whole-slide scanners opened a wide range of possibilities for addressing challenging image analysis problems, includ- ing the identification of tissue-based biomarkers. Whole-slide scanners are devices that are able to image whole tissue slides at resolutions up to 0.1 micrometers per pixels, often referred to as virtual slides. In addition to quantification of IHC staining patterns, virtual slides are invaluable tools for the implementation of digital pathology work- flows. The present work aims to make several contributions towards this current digital shift in pathology. Our first contribution was to propose an automated virtual slide sharpness assessment tool. Although modern whole-slide scanner devices resolve most image standardization problems, focusing errors are still likely to be observed, requiring a sharpness assessment procedure. Our proposed tool will ensure that images provided to subsequent pathologist examination and image analysis are correctly focused. Virtual slides also enable the characterization of biomarker expression heterogeneity. Our sec- ond contribution was to propose a method to characterize the distribution of densely stained regions in the case of nuclear IHC biomarkers, with a focus on the identification of highly proliferative tumor regions by analyzing Ki67-stained tissue slides. Finally, as a third contribution, we propose an efficient mean to register virtual slides in order to characterize biomarker colocalization on adjacent tissue slides. This latter contribution opens new prospects for the analysis of more complex questions at the tissue level and for finely characterizing disease processes and/or treatment responses./Les protéines sont les véritables acteurs des processus physiologiques (normaux ou per- turbés) et l’immunohistochimie (IHC) est un moyen efficace pour visualiser et localiser leur expression au sein d’échantillons histologiques. En comparant des échantillons de tissus pathologiques et normaux, l’IHC permet de révéler des altérations dans des pro- fils d’expression protéique. C’est pourquoi l’IHC joue un rôle de plus en plus important pour mettre en évidence des biomarqueurs histologiques intervenant dans le diagnos- tic de diverses pathologies et dans le choix de thérapies personnalisées. L’évaluation de l’expression de biomarqueurs révélés par IHC a donc des répercussions importantes sur l’adéquation des choix thérapeutiques pour les patients souffrant de pathologies graves, comme le cancer. Cependant, cette évaluation peut être chronophage et difficile à appliquer en pratique, d’une part, à cause de l’hétérogénéité de l’expression protéique intra- et inter-échantillon, d’autre part, du fait de l’absence de critères de positivité bien définis. Il est donc devenu crucial de quantifier les profils d’expression de marquages IHC en histopathologie. A cette fin, l’analyse d’image automatisée possède de multiples avantages, comme celui d’éviter les effets de la subjectivité humaine, déjà démontrés par ailleurs. L’apparition récente des numériseurs de lames histologiques complètes, ou scanners de lames, a permis l’émergence d’un large éventail de possibilités pour traiter des problèmes d’analyse d’image difficiles menant à l’identification de biomar- queurs histologiques. Les scanners de lames sont des dispositifs capables de numériser des lames histologiques à une résolution pouvant atteindre 0,1 micromètre par pixel, expliquant la dénomination de "lames virtuelles" des images ainsi acquises. En plus de permettre la quantification des marquages IHC, les lames virtuelles sont des outils indis- pensables pour la mise en place d’un flux de travail numérique en pathologie. Le travail présenté ici vise à fournir plusieurs contributions au récent changement de cap vers une numérisation de la discipline médicale qu’est l’anatomie pathologique. Notre première contribution consiste en un outil permettant d’évaluer automatiquement la netteté des lames virtuelles. En effet, bien que les scanners de lames résolvent la plupart des pro- blèmes liés à la standardisation de l’acquisition, les erreurs de focus restent fréquentes, ce qui nécessite la mise en place d’une procédure de vérification de la netteté. L’outil que nous proposons assurera la netteté des images fournies à l’examen du pathologiste et à l’analyse d’image. Les lames virtuelles permettent aussi de caractériser l’hétérogénéité de l’expression de biomarqueurs. Ainsi, la deuxième contribution de ce travail repose sur une méthode permettant de caractériser la distribution de régions densément marquées par des biomarqueurs IHC nucléaires. Pour ce travail, nous nous sommes concentrés sur l’identification de régions tumorales présentant une forte activité proliférative en analysant des lames virtuelles révélant l’expression de la protéine Ki67. Finalement, la troisième contribution de ce travail fut de proposer un moyen efficace de recaler des lames virtuelles dans le but de caractériser la colocalisation de biomarqueurs IHC révé- lés sur des coupes de tissu adjacentes. Cette dernière contribution ouvre de nouvelles perspectives pour l’analyse de questions complexes au niveau histologique ainsi que la caractérisation fine de processus pathologiques et de réponses thérapeutiques.<br>Doctorat en Sciences de l'ingénieur<br>info:eu-repo/semantics/nonPublished
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Ganapathi, Senthil Kumar. "Expression and DNA methylation of TNF, IFNG and FOXP3 in colorectal cancer, and association with clinico-pathological features." Thesis, St George's, University of London, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.676102.
Full textAbstract:
Colorectal cancer (CRC) progression is associated with suppression of host cell mediated immunity (CMI) and local immune escape mechanisms. Suppression of CMI associated cytokines (IFN-y & TNF-a) has been demonstrated in CRC. Immunosuppressive effect of T regulatory cells in solid cancers has been demonstrated with FOXP3 being identified as the most specific T regulatory cell marker. To assess the impact of immunological function upon survival in CRC and investigate the expression of TNF, IFNG & FOXP3 and their epigenetic regulation in CRC. Methods Sixty patients with CRC were recruited. TaqMan quantitative PCR (qRT-PCR) was performed for relative quantitation of expression of TNF, IFNG and FOXP3 in the PBMC and tumour samples. Methylation specific PCR (MSPCR) was performed to determine the DNA methylation status. Survival analysis was performed using Kaplan-Meier method and Cox regression models. Results TNF was suppressed in tumour tissue and IFNG was suppressed in the PBMC of patients with CRC. IFNG suppression in PBMC was significant in recurrent CRC. Tumour tissue showed enhanced expression of FOXP3 and had a positive correlation with tumour size. Methylated TNFprom0 ter, TNFexonl and FOXP3cpg correlated significantly with suppression of TNF and FOXP3 respectively. Significant TNF suppression was noted in the PBMC of patients with MSI. A suppressed FOXP3 in PBMC was associated with poor overall survival. Tumour vascular invasion (VI) was associated with enhanced expression of TNF. While VI is associated with adverse prognosis in CRC, its presence is an independent predictor of poor survival in younger patients. Conclusion Specific patterns of TNF, IFNG and FOXP3 expression correlate with tumour progression. DNA methylation appears to influence the expression of TNF and FOXP3. Further quantitative analyses are needed to confirm the findings. We have detected changes in the immunomodulatory genes that could act as biomarkers for prognosis and future immunotherapeutic strategies.
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Tanaka, Yuichi. "Intragraft expression of recipient-type ABO blood group antigens : long-term follow-up and histological features after liver transplantation." Kyoto University, 2006. http://hdl.handle.net/2433/143871.
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Phan, John H. "Biomarker discovery and clinical outcome prediction using knowledge based-bioinformatics." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33855.
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Advances in high-throughput genomic and proteomic technology have led to a growing interest in cancer biomarkers. These biomarkers can potentially improve the accuracy of cancer subtype prediction and subsequently, the success of therapy. However, identification of statistically and biologically relevant biomarkers from high-throughput data can be unreliable due to the nature of the data--e.g., high technical variability, small sample size, and high dimension size. Due to the lack of available training samples, data-driven machine learning methods are often insufficient without the support of knowledge-based algorithms. We research and investigate the benefits of using knowledge-based algorithms to solve clinical prediction problems. Because we are interested in identifying biomarkers that are also feasible in clinical prediction models, we focus on two analytical components: feature selection and predictive model selection. In addition to data variance, we must also consider the variance of analytical methods. There are many existing feature selection algorithms, each of which may produce different results. Moreover, it is not trivial to identify model parameters that maximize the sensitivity and specificity of clinical prediction. Thus, we introduce a method that uses independently validated biological knowledge to reduce the space of relevant feature selection algorithms and to improve the reliability of clinical predictors. Finally, we implement several functions of this knowledge-based method as a web-based, user-friendly, and standards-compatible software application.
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Mushfieldt, Diego. "Robust facial expression recognition in the presence of rotation and partial occlusion." Thesis, University of Western Cape, 2014. http://hdl.handle.net/11394/3367.
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>Magister Scientiae - MSc<br>This research proposes an approach to recognizing facial expressions in the presence of
rotations and partial occlusions of the face. The research is in the context of automatic
machine translation of South African Sign Language (SASL) to English. The proposed
method is able to accurately recognize frontal facial images at an average accuracy of
75%. It also achieves a high recognition accuracy of 70% for faces rotated to 60◦. It was
also shown that the method is able to continue to recognize facial expressions even in
the presence of full occlusions of the eyes, mouth and left/right sides of the face. The
accuracy was as high as 70% for occlusion of some areas. An additional finding was that
both the left and the right sides of the face are required for recognition. As an addition,
the foundation was laid for a fully automatic facial expression recognition system that
can accurately segment frontal or rotated faces in a video sequence.
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Kamath, Vidya. "Use of Random Subspace Ensembles on Gene Expression Profiles in Survival Prediction for Colon Cancer Patients." Scholar Commons, 2005. https://scholarcommons.usf.edu/etd/715.
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Cancer is a disease process that emerges out of a series of genetic mutations that cause seemingly uncontrolled multiplication of cells. The molecular genetics of cells indicates that different combinations of genetic events or alternative pathways in cells may lead to cancer. A study of the gene expressions of cancer cells, in combination with the external influential factors, can greatly aid in cancer management such as understanding the initiation and etiology of cancer, as well as detection, assessment and prediction of the progression of cancer.
Gene expression analysis of cells yields a very large number of features that can be used to describe the condition of the cell. Feature selection methods are explored to choose the best of these features that are most relevant to the problem at hand. Random subspace ensembles created using these selected features perform poorly in predicting the 36-month survival for colon cancer patients. A modification to the random subspace scheme is proposed to enhance the accuracy of prediction. The method first applies random subspace ensembles with decision trees to select predictive features. Then, support vector machines are used to analyze the selected gene expression profiles in cancer tissue to predict the survival outcome for a patient.
The proposed method is shown to achieve a weighted accuracy of 58.96%, with 40.54% sensitivity and 77.38% specificity in predicting 36-month survival for new and unknown colon cancer patients. The prediction accuracy of the method is comparable to the baseline classifiers and significantly better than random subspace ensembles on gene expression profiles of colon cancer.
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Hu, Haiyang [Verfasser]. "Computational and Statistical Analysis of Sequence and Expression Features of MicroRNA and Long Noncoding RNA in Primate Brains / Haiyang Hu." Berlin : Freie Universität Berlin, 2016. http://d-nb.info/1093404175/34.
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Huang, X. (Xiaohua). "Methods for facial expression recognition with applications in challenging situations." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526206561.
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Abstract In recent years, facial expression recognition has become a useful scheme for computers to affectively understand the emotional state of human beings. Facial representation and facial expression recognition under unconstrained environments have been two critical issues for facial expression recognition systems. This thesis contributes to the research and development of facial expression recognition systems from two aspects: first, feature extraction for facial expression recognition, and second, applications to challenging conditions. Spatial and temporal feature extraction methods are introduced to provide effective and discriminative features for facial expression recognition. The thesis begins with a spatial feature extraction method. This descriptor exploits magnitude while it improves local quantized pattern using improved vector quantization. It also makes the statistical patterns domain-adaptive and compact. Then, the thesis discusses two spatiotemporal feature extraction methods. The first method uses monogenic signal analysis as a preprocessing stage and extracts spatiotemporal features using local binary pattern. The second method extracts sparse spatiotemporal features using sparse cuboids and spatiotemporal local binary pattern. Both methods increase the discriminative capability of local binary pattern in the temporal domain. Based on feature extraction methods, three practical conditions, including illumination variations, facial occlusion and pose changes, are studied for the applications of facial expression recognition. First, with near-infrared imaging technique, a discriminative component-based single feature descriptor is proposed to achieve a high degree of robustness and stability to illumination variations. Second, occlusion detection is proposed to dynamically detect the occluded face regions. A novel system is further designed for handling effectively facial occlusion. Lastly, multi-view discriminative neighbor preserving embedding is developed to deal with pose change, which formulates multi-view facial expression recognition as a generalized eigenvalue problem. Experimental results on publicly available databases show that the effectiveness of the proposed approaches for the applications of facial expression recognition<br>Tiivistelmä Kasvonilmeiden tunnistamisesta on viime vuosina tullut tietokoneille hyödyllinen tapa ymmärtää affektiivisesti ihmisen tunnetilaa. Kasvojen esittäminen ja kasvonilmeiden tunnistaminen rajoittamattomissa ympäristöissä ovat olleet kaksi kriittistä ongelmaa kasvonilmeitä tunnistavien järjestelmien kannalta. Tämä väitöskirjatutkimus myötävaikuttaa kasvonilmeitä tunnistavien järjestelmien tutkimukseen ja kehittymiseen kahdesta näkökulmasta: piirteiden irrottamisesta kasvonilmeiden tunnistamista varten ja kasvonilmeiden tunnistamisesta haastavissa olosuhteissa. Työssä esitellään spatiaalisia ja temporaalisia piirteenirrotusmenetelmiä, jotka tuottavat tehokkaita ja erottelukykyisiä piirteitä kasvonilmeiden tunnistamiseen. Ensimmäisenä työssä esitellään spatiaalinen piirteenirrotusmenetelmä, joka parantaa paikallisia kvantisoituja piirteitä käyttämällä parannettua vektorikvantisointia. Menetelmä tekee myös tilastollisista malleista monikäyttöisiä ja tiiviitä. Seuraavaksi työssä esitellään kaksi spatiotemporaalista piirteenirrotusmenetelmää. Ensimmäinen näistä käyttää esikäsittelynä monogeenistä signaalianalyysiä ja irrottaa spatiotemporaaliset piirteet paikallisia binäärikuvioita käyttäen. Toinen menetelmä irrottaa harvoja spatiotemporaalisia piirteitä käyttäen harvoja kuusitahokkaita ja spatiotemporaalisia paikallisia binäärikuvioita. Molemmat menetelmät parantavat paikallisten binärikuvioiden erottelukykyä ajallisessa ulottuvuudessa. Piirteenirrotusmenetelmien pohjalta työssä tutkitaan kasvonilmeiden tunnistusta kolmessa käytännön olosuhteessa, joissa esiintyy vaihtelua valaistuksessa, okkluusiossa ja pään asennossa. Ensiksi ehdotetaan lähi-infrapuna kuvantamista hyödyntävää diskriminatiivistä komponenttipohjaista yhden piirteen kuvausta, jolla saavutetaan korkea suoritusvarmuus valaistuksen vaihtelun suhteen. Toiseksi ehdotetaan menetelmä okkluusion havainnointiin, jolla dynaamisesti havaitaan peittyneet kasvon alueet. Uudenlainen menetelmä on kehitetty käsittelemään kasvojen okkluusio tehokkaasti. Viimeiseksi työssä on kehitetty moninäkymäinen diskriminatiivisen naapuruston säilyttävään upottamiseen pohjautuva menetelmä käsittelemään pään asennon vaihtelut. Menetelmä kuvaa moninäkymäisen kasvonilmeiden tunnistamisen yleistettynä ominaisarvohajotelmana. Kokeelliset tulokset julkisilla tietokannoilla osoittavat tässä työssä ehdotetut menetelmät suorituskykyisiksi kasvonilmeiden tunnistamisessa
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Anužytė, Reda. "Tradicinės ir modernios vizualinės raiškos mokymo ypatumai 5–6 klasių dailės pamokose." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2011. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20110627_140302-64612.
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1991 m. prasidėjusi Lietuvos švietimo sistemos reforma iškėlė daug spręstinų klausimų tiek ugdymo praktikams, tiek teoretikams. Buvo pastebėta, kad skirtingi ugdymo tikslai vizualinėje raiškoje reikalauja skirtingų dailės ugdymo ypatumų. Tradicinės ar modernios vizualinės raiškos mokymas gali apspręsti skirtingą mokinių pasiekimų lygį, formuoti skirtingus mokinių estetinius idealus, požiūrį į dailės ir meno reiškinius ir kt. Remiantis dailės istorijos, estetikos bei ugdymo teorijų literatūros šaltinių analize, darbe buvo iškeltas tikslas ištirti tradicinės ir modernios vizualinės raiškos mokymo ypatumus 5–6 klasių dailės pamokose. Tikslui pasiekti įvadinėje darbo dalyje buvo suformuluoti pagrindiniai uždaviniai: išanalizuoti aktualias estetikos teorijas bei dailės istorijos šaltinius, siekiant atskleisti tradicinės ir modernios dailės sampratą; nustatyti vizualinės raiškos ugdymo procesą lemiančius veiksnius; aptarti 5–6 klasių mokinių vizualinės raiškos ypatumus; išsiaiškinti priežastis, skatinančias tradicinės ir modernios vizualinės raiškos ugdymą. Tyrimu buvo siekiama atskliesti tradicinės ir modernios vizualinės raiškos mokymo ypatumus apsprendžiančias priežastis mokytojų ir mokinių požiūriu.
Pirmojoje darbo dalyje buvo atskleista tradicinės ir modernios dailės samprata dailės istorijos bei estetinių teorijų kontekste, apžvelgiant meninių srovių atsiradimą, stilistinius ypatumus, estetinius siekius, aiškinantis įvairių srovių panašumus bei skirtumus.
Antrojoje darbo... [toliau žr. visą tekstą]<br>The reform of the Lithuanian educational system, which was initiated in 1991, raised many unresolved issues for both educators and theoreticians in the field of education to deal with. The attention was paid to the fact that different educational objectives in the visual expression require for different approaches to art education. However, teaching traditional and modern visual expression can determine different levels of students’ achievements and shape different aesthetic ideals and attitudes towards the forms of art or the arts, etc. Following the analysis of the sources on the history of art, aesthetics and theories of education, the research paper aims at examining the characteristics of teaching traditional and modern visual expression in art classes for 5 and 6 grade students. With this end in view, the introduction of the research paper sets forth the main tasks: to study the relevant theories on aesthetics and education in order to present the conceptions of traditional art and modern art; to identify the factors leading to the teaching traditional or modern visual expression. The survey aims at establishing the causes which determine the characteristics of teaching traditional or modern visual expression from the viewpoint of students and teachers.
The first part of the research paper presents the conceptions of traditional and modern art in the context of the history of art and theories on aesthetics by taking a look at the birth of art movements, their stylistic... [to full text]
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Xu, Yaomin. "New Clustering and Feature Selection Procedures with Applications to Gene Microarray Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1196144281.
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Holmberg, Mattias. "A Construction Grammar Analysis of the expression /on the one hand...on the other hand/." Thesis, Stockholm University, Department of English, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-8124.
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<p>The expression /on the one hand…on the other hand/ (OH1 OH2) is a fixed linguistic pattern which is used to emphasize the comparison between two possibly complex propositions (henceforth X and Y). The static syntactic form of the pattern and the specific semantic comparison it evokes are strong indicators that it is a construction of the type discussed in the analytical method Construction Grammar (henceforth CxG). Thus, the aim of this essay is to argue that the pattern OH1X OH2Y is a CxG construction with specific syntactic and semantic constraints, and at the same time to give a descriptive account of the features of the construction. The British National Corpus was used to get examples containing the pattern. The syntactic and semantic features of these examples were analysed and the results were compared with how the traditional descriptive grammarians account for the pattern.</p>
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Федорова, Дар’я Олександрівна, та Daria Oleksandrivna Fedorova. "Вигук як гендерна особливість мовленнєвої поведінки у сучасному американському графічному дискурсі". Master's thesis, 2020. http://repository.sspu.edu.ua/handle/123456789/9737.
Full textAbstract:
У роботі прослідковується взаємозв’язок категорії статі і мови та аналізуються існуючі гендерні стереотипи. Робиться системний аналіз особливостей вживання вигуку із позицій автора-чоловіка та автора-жінки у американському графічному дискурсі.<br>The paper traces the relationship between gender and language and analyzes existing gender stereotypes. A systematic analysis of the peculiarities of the use of the exclamation from the positions of the author-man and the author-woman in the American graphic discourse is made.
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Vojtěch, Albert. "Komplexní slova typu 'absobloominlutely'." Master's thesis, 2019. http://www.nusl.cz/ntk/nusl-393648.
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iv Abstract The MA thesis examines the word-formation potential of expletive insertion with simple and complex words in English. It represents a linguistic phenomenon that is commonly used by native speakers, shows a certain degree of regularity and has gained popularity with the rise of the Internet, social media and the movie industry. The theoretical part introduces the previous studies on the phenomenon and presents the basic features of the phenomenon, namely the categorization of inserts and the classification of their positions in terms of the structure of the base as outlined by McMillan (1980). The extraction of the sample is described in the methodology section. The empirical part examines the phenomenon's main principles of use governed by prosody and morphology and illustrates the properties and both regularities and irregularities that the process exhibits (predictable insert position, poly-syllabicity of the base, its unchanged meaning and syntactic category, alternative categories of input bases and morphematic discontinuity of bases). The analysis comprises of two main parts: the study of the inserted bases (word-class, type of base, simple vs. complex, and a number of syllables) and the study of the expletive insert (representation of individual inserts and their position relative to stress...
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Cunha, Adriana Monteiro e. "Neural networks for 2D representations of cell expression." Master's thesis, 2020. http://hdl.handle.net/10316/93918.
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Trabalho de Projeto do Mestrado Integrado em Engenharia Biomédica apresentado à Faculdade de Ciências e Tecnologia<br>The recent advances in transcriptome sequencing technologies lead to the increase of gene expression studies, with significant impact in the fields of cellular biology and medicine. Typically, the work developed based on this type of data resorts to feature reduction techniques to combat the problems risen by the curse of dimensionality and from data extraction (such as dropout events, noise, etc.), especially in projects involving classification tasks. This dissertation presents a novel dimensionality reduction model inspired by deep neural networks, the Supervised Autoencoder, which combines the architecture of traditional autoencoders with a SoftMax classification layer, so the latent space maximizes different classes’ separability. To account for the recurring dropout events in this type of datasets, a Dropout layer was implemented during training, improving the model’s robustness. The present study focuses particularly on two-dimensional reductions to ease the information’s visualisation. In addition to an analysis of the effect of label usage in the feature reduction process (prior to potential classification tasks), the possibility of inferring new similarity patterns between samples through the latent space was explored.The model was validated with three datasets, comparing its results with those of Principal Component Analysis and the equivalent simple autoencoder, as well as by analysing the heatmap of the complete gene expression clustered based on the engineered features. The results show the model is capable of meaningful representations of the original data that ease the classification task compared to the ones resultant of state-of-the-art techniques. However, it is not possible to draw new parallels between samples based on those features.<br>Os recentes avanços nas tecnologias de sequenciação do transcriptoma humano levaram ao aumento de estudos baseados em dados de expressão genética, com notável impacto nas áreas da biologia e medicina. Tipicamente, o trabalho desenvolvido com base neste tipo de informação recorre a técnicas de redução de features para combater os problemas que advêm da curse of dimensionality e associados à extração de dados de expressão (como eventos de dropout, ruído, etc.), sobretudo em projetos com tarefas de classificação.Nesta dissertação apresenta-se um modelo de redução de dimensionalidade inspirado em redes neuronais, o Autoencoder Supervisionado, que acopla a arquitetura tradicional de autoencoders com uma camada de classificação SoftMax, para que as representações no espaço latente maximizem a separabilidade entre diferentes classes. De forma a considerar os recorrentes eventos dropout neste tipo de dados, foi usada uma camada Dropout na fase de treino, conferindo maior robustez ao modelo. O estudo em causa foca-se em particular em reduções para duas dimensões, de forma a facilitar a visualização gráfica de informação. Além da análise do efeito da contabilização de classes no processo de redução de features (a priori de potenciais tarefas de classificação), explorou-se a possibilidade de o espaço latente obtido permitir aferir novos padrões de semelhança entre amostras.O modelo foi validado usando três conjuntos de dados, comparando os seus resultados com os obtidos através de Principal Component Analysis e do autoencoder simples equivalente, bem como através da análise do mapa de calor dos dados completos de expressão genética agrupados através do clustering hierárquico das features reduzidas.Os resultados mostram que o modelo é capaz de gerar representações adequadas dos dados originais, que permitem facilitar a tarefa de classificação quando comparadas com as resultantes das técnicas estado-da-arte. No entanto, não foi possível utilizá-las para estabelecer novos paralelos entre amostras.<br>Outro - Projeto financiado pela Fundação para a Ciência e Tecnologia: D4 - Deep Drug Discovery and Deployment (CENTRO-01-0145-FEDER-029266)
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Chuang, YungChuan, and 莊詠筌. "Facial Expression Mapping Based on Facial Features." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/65155250635080513268.
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碩士<br>國立臺北教育大學<br>數位科技設計學系(含玩具與遊戲設計碩士班)<br>99<br>With the advancement of computer technology, the communication and
interaction between people was enhanced through instant messaging softwares.
However, users who communicate via these softwares can not reveal their ‘true’
emotions with their own facial expression but through the use of emoticons. Yet, these
emoticons could not fully express facial expressions. Inspired by this shortcoming, if a
specific user’s face can automatically make facial expressions, instant messaging would
be more interesting.
The purpose of this thesis is to make any specific type or user-defined facial
expressions on images of neutral facial expression by image processing techniques. Two
phases of process is carried out to serve this purpose: image pre-processing and feature
extraction , and expression mapping. The main purpose of image pre-processing is to
determine face region based on skin-tone segmentation and morphological operations.
After that, localize expression features by their color information, viz.: brows, eyes, and
mouth. Then, set its shape control points which are simplified from the definition of
Facial Animation Parameters (FAP) by MPEG-4 standard. In the expression mapping
phase, users can move those control points via an interactive interface. When this
procedure is finished, the system changes the selected image texture to fit a new shape
by Delaunay triangulation, image registration, and image interpolation operations.
Finally, the result of a new facial expression on the original image is generated.
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Hsu, Wei-Cheng, and 徐瑋呈. "Facial Expression Recognition Based on Facial Features." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/50258463357861831524.
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碩士<br>國立清華大學<br>資訊工程學系<br>101<br>We propose an expression recognition method based on facial features from the psychological perspective. According to the American psychologist Paul Ekman’s work on action units, we divide a face into different facial feature regions for expression recognition via the movements of individual facial muscles during slight different instant changes in facial expression. This thesis starts from introducing Paul Ekman’s work, 6 basic emotions, and existing methods based on feature extraction or facial models. Our system have two main parts: preprocessing and recognition method.
The difference in training and test environments, such as illumination, or face size and skin color of different subjects under testing, is usually the major influencing factor in recognition accuracy. It is therefore we propose a preprocessing step in our first part of the system: we first perform face detection and facial feature detection to locate facial features. We then perform a rotation calibration based on the horizontal line obtained by connecting both eyes. The complete face region can be extracted by using facial models. Lastly, the face region is calibrated for illumination and resized to same resolution for dimensionality of feature vector.
After preprocessing, we can reduce the difference among images. Second part of our proposed system is the recognition method. Here we use Gabor filter banks with ROI capture to obtain the feature vector and principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction to reduce the computation time. Finally, a support vector machine (SVM) is adopted as our classifier.
The experimental result shows that the proposed method can archive 86.1%, 96.9%, and 89.0% accuracy on three existing datasets JAFFE, TFEID, and CK+ respectively (based on leave-one-person-out evaluation). We also tested the performance on the 101SC dataset that were collected and prepared by ourselves. This dataset is relatively difficult in recognition but closer to the scenario in reality. The proposed method is able to achieve 62.1% accuracy on it. We also use this method to participate the 8th UTMVP (Utechzone Machine Vision Prize) competition, and we were ranked the second place out of 10 teams.
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Pan, De-Cheng, and 潘德城. "Integrating global feature and local texture feature for facial expression recognition." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/49511168553467722535.
Full textAbstract:
碩士<br>國立臺北大學<br>通訊工程研究所<br>95<br>Facial expression is a representation of human internal emotion. It plays a important role in the interaction in the daily life. Most common facial expression recognitions use singlefeature to represent a facial image. It is insufficient in some specific face images, and will make system unstable. Thus, this thesis presents a multi-feature model to represent a facial image, which adopts wavelet-based two dimensional principal component analysis (2DPCA) and local binary pattern (LBP) for our integrated feature. By combining two features, the recognition system of multi-feature model accepts the merit of each feature, which outperforms single-feature model. A linear weighting strategy is suggested for the feature concatenation.
The proposed method is evaluated on three facial expression database: Yale, JAFFE and NimStim. Experimental results reveal that our method is successful in improving the recognition accuracy.
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Yeh, Shang-Min, and 葉尚旻. "Heterogeneous Feature Codes for Expression Recognition." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/38673092137922743424.
Full textAbstract:
碩士<br>國立臺灣科技大學<br>機械工程系<br>101<br>The Heterogeneous Feature Code (HFC), a coding scheme based on both human and machine selected local features, is proposed for expression recognition. The HFC consists of two component codes, the Human Observable Code (HOC) and Boost Feature Code (BFC). The HOC is developed to capture the local deformation patches observable to humans when the face is showing an expression. Different expressions appear with a specific set of such patches with different deformation patterns at different locations, which are considered in the configuration of the HOC codewords. The BFC is built upon the local texture features selected by a set of AdaBoost classifiers followed by a multi-class SVM classifier. The HFC is the combination of HOC and BFC by the Bayesian Rule, and is proven effective for expression recognition. Performance evaluation on the Cohn-Kanade extension (CK+) database, the Japanese Female Facial Expression (JAFFE) database, and the GEMEP-FERA dataset shows that the HFC outperforms either HOC or BFC component code alone, and is competitive to the state-of-the-art.
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Neath, Karly. "The use of facial features in facial expression discrimination." Thesis, 2012. http://hdl.handle.net/10012/6577.
Full textAbstract:
The present four studies are the first to examine the effect of presentation time on accurate facial expression discrimination while concurrently using eye movement monitoring to ensure fixation to specific features during the brief presentation of the entire face. Recent studies using backward masking and evaluating accuracy performance with signal detection methods (A’) have identified a happy-face advantage however differences between other facial expressions of emotion have not been reported. In each study, a specific exposure time before mask (150, 100, 50, or 16.67 ms) and eight different fixation locations were used during the presentation of neutral, disgusted, fearful, happy, and surprised expressions. An effect of emotion was found across all presentation times such that the greatest performance was seen for happiness, followed by neutral, disgust, surprise, and with the lowest performances seen for fear. Fixation to facial features specific to an emotion did not improve performance and did not account for the differences in accuracy performance between emotions. Rather, results suggest that accuracy performance depends on the integration of facial features, and that this varies across emotions and with presentation time.
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Loi, Ka Chon, and 雷嘉駿. "Automatic Generation of Human Animation for Expressing Music Features." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/55587421591920130926.
Full textAbstract:
碩士<br>國立政治大學<br>資訊科學學系<br>96<br>In recent years, the improvement of computing ability has contributed to the wide application of 3D virtual environment. In the thesis, we propose to combine character animation with music for music interpretation in 3D virtual environment. The system proposed in the thesis is an intelligent avatar motion generator, which generates expressive motions according to music features. The system can extract music features from input music data, segment a music into several music segments, and then plan avatar animation. In the literature, much music-related animation research uses reconstruction and modification of existing motion to compose new animations. In this work, we analyze the relationship between music and motions, and then use procedural animation to automatically generate applicable and variable motions to interpret music. Our experiments show that the system can accept LOA1 models and midi as inputs in general, and generate appropriate expressive motions by modifying parameters according to users’ preference or music style.
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Chao, Chia-Huang, and 趙嘉煌. "Feature Selection for Microarray Gene Expression Data." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/18135104235078213487.
Full textAbstract:
碩士<br>國立臺灣科技大學<br>資訊工程系<br>92<br>Feature selection plays an extremely important role in many data mining tasks. In this thesis, we applied three feature selection approaches, weight score approach, the 1-norm SVM and IRSVM to microarray gene expression data classification on two well-known datasets, acute leukemia and colon cancer datasets. We introduced the correlation coefficient criterion to evaluate these three feature selection approaches. The weight score approach selects the significant features independently. As a result, the highly linear correlated features might be selected. While the 1-norm SVM and IRSVM select features under the classification mechanism and will exclude the highly linear correlated features. Besides, the 1-norm SVM and IRSVM selected fewer features than weight score did. We applied the SSVM to these resulting selected feature sets respectively and got a slightly better classification result on the case of 1-norm SVM and IRSVM.
In another part of our experiments, we iteratively remove selected features from original datasets and re-perform feature selection and classification steps until the classification accuracy degrades drastically. We find that more than one feature subset can be used to construct SSVM classifiers with similar classification accuracy in each dataset for every feature selection approach scheme. This result indicates that there are several feature subsets can provide enough information for classification tasks in a microarray gene expression dataset.
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LI, CHENG-XUAN, and 李成軒. "Automatic Multi-feature Facial Expression Recognition System." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/t9jvz5.
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碩士<br>國立中正大學<br>資訊管理系研究所<br>107<br>With the development of science and technology, and the change of human structure, people begin to pursue higher quality of life and the requirements in social welfare, medical care, home care, education and other services are also increasing. Facial expression recognition plays an important role in a variety of applications, such as human-computer interaction (HCI), robot control, and driver state surveillance. Hence, how to extract emotional features accurately is critical for facial expression recognition. This paper mainly combines three kinds of methods to extract facial features. The first one is based on the facial action coding system (FACS), and we capture distance features after the combination of action units (AU). Second, to extract histograms of block-based local binary pattern (B-LBP) features. Third, feature extraction of histogram of oriented gradient (HOG) on each face image. Among them, B-LBP and HOG features will use principal component analysis (PCA) to reduce dimensionality. Finally, we input the three features into support vector machine (SVM) for expression recognition. The experimental results demonstrate high correct recognition rate, and effectively reduce the negative impact on the overall performance.
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Mersmann, Ryan S. "Relating the Expression of Soil Redoximorphic Features to Soil Texture, pH, and Cation Exchange Capacity." 2009. http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-3260.
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Three laboratory studies were performed to elucidate the influence of soil
texture, pH, and cation exchange capacity (CEC) on the concentration of ferrous Fe in
soil solution and the resulting expression of soil redoximorphic features. The objectives
were: 1) assess the buffering effects of CEC on ferrous Fe concentration in soil solution,
2) evaluate the effects of pH on the concentration of ferrous Fe in soil solution, and 3)
observe the expression of redoximorphic features in soils with varying texture and CEC.
The studies concentrated on seasonally wet soils from the Texas Gulf Coast
Prairie. Selected soils included Alfisols and Vertisols with characteristics ranging from
coarse-loamy to very-fine in texture, strongly acidic to neutral in soil reaction, and
siliceous, mixed, and smectitic in mineralogy. The soils included the Pledger clay
microlow (acidic, fine-textured), Pledger clay microhigh (neutral, fine-textured), China
clay (acidic, fine-textured), Cieno loam (acidic, fine-loamy), Orelia sandy clay loam
(neutral, fine-loamy), Gessner fine sandy loam (acidic, coarse-loamy), and Orelia fine
sandy loam (neutral, coarse-loamy).
The studies provided the following information: 1) fine-textured soils with
higher CEC contained more ferrous Fe in solution, 2) ferrous Fe concentrations in the
acidic fine-loamy and coarse-loamy soils were higher than the neutral soils for the same textural class, 3) acidic and neutral fine-textured soils contained more ferrous Fe in
solution than the remaining soils, 4) the highest percentage of redox concentrations was
observed in the acidic, fine-textured soil, 5) the acidic fine-loamy and coarse-loamy soils
exhibited a greater percentage of Fe depletions, and 6) a higher percentage of redox
features were observed by micromorphic analysis (i.e., point counts under a binocular
stereoscopic microscope) than by macromorphic descriptions. This research showed
that differing soil characteristics affect the reductive dissolution and translocation of Fe,
and subsequent formation of redox features.
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Ko, Chung-hung, and 柯忠宏. "Facial Expression Recognition Based on Two-Stage Local Texture Features." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/41743221509632503474.
Full textAbstract:
碩士<br>國立高雄大學<br>電機工程學系碩士班<br>101<br>Facial expression recognition plays an important role in a variety of applications such as human-computer interaction, robot control, and driver state surveillance. One critical step for facial expression recognition is to accurately extract emotional features. In this paper, we proposed a facial expression recognition approach based on two-stage local facial textures. We use the threshold local binary pattern to transform a facial image into a feature image, and then we extract the most discriminate features based on the block-based center-symmetric local binary pattern. These features are classified by using the support vector machine. Experiment results show the effectiveness of the proposed approach on both the Cohn-Kanade and JAFFE databases.
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Hsieh, Hung-chiang, and 謝鴻瑲. "Facial Expression Recognition Based on Salient Local Texture-based Features." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/51647322680031512087.
Full textAbstract:
碩士<br>國立高雄大學<br>電機工程學系碩士班<br>102<br>Automatic facial expression recognition has gained an increasing interest in recent years in building natural human-computer interaction systems. Extracting the discriminative features from facial images is the most important part of facial expression recognition. In this paper, we propose a facial expression recognition approach based on salient local texture-based features. We use Speeded Up Robust Features to find interest points and to generate salient regions from facial images, and then the associated features based on Center-Symmetric Local Ternary Pattern and Local Sign Directional Pattern in these regions are extracted. These features are classified by using the Support Vector Machine. Experimental results show that our approach can achieve higher recognition rate on the Cohn-Kanade, Extended Cohn-Kanade, TFEID, and JAFFE facial expression databases.
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Huang, Chiao-Wen, and 黃巧妏. "Human Facial Expression Recognition with Multi-Feature Model." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/29672349100249037359.
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碩士<br>中華大學<br>資訊工程學系(所)<br>96<br>Identifying human facial expressions on still image is a challenging task in computer vision research. We have presented a multi-feature model capable of recognizing facial expressions. The proposed model adopts wavelet transform based two-dimensional principal component analysis (W-2DPCA), which performs better than traditional PCA and local binary pattern (LBP), which is efficient for analyzing the texture of a image. In this paper, we further proposed
an edge-based LBP (ELBP) and improved the recognition accuracy. A weighted combination of 2DPCA and ELBP features is input to the decision directed acyclic graph (DDAG) based support vector machine (SVM) classifier. Thus, the mixed-feature model has the advantages of both features. The proposed method is measured on six facial expression databases, AR, CohnKanade, JAFFE, GUR, Nimstim and Yale, and is compared with other methods. Experimental results indicate that the proposed model is feasible and the identification rate outperforms other methods.
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Hsu, Chin-Hao, and 徐金豪. "Study of Modified Active Appearance Models Features for Facial Expression Recognition." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/57597341704418922720.
Full textAbstract:
碩士<br>國立臺灣科技大學<br>資訊工程系<br>98<br>The six basic facial expressions (anger, disgust, fears, happy, sadness, surprise) which have been recognized in psychology are commonly used for facial expressions recognition research. In this work, we propose intuitive and effective facial features to recognize the six facial expressions by modifying the original facial feature points used by the original Active Appearance Model (AAM). The Support Vector Machine (SVM) is employed as the base classifier for facial expression classification. The original facial features are two dimensional vertex point position represented by the x- and y- coordinates. However, we observe that every facial expression is just a deviation from the original neutral facial expression, so we try to exploit the differences between any facial expression and the neutral expression for facial expression recognition. The experiment results by applying the proposed differential facial feature, the recognition accuracy is higher than by applying the original facial features for all facial expressions.
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Fu, Hsu-Yuan, and 傅煦媛. "Expression of Six Photoreceptors in Haloarcula marismortui Unveiled Unique Photosensing Features." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/09314255350786937818.
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碩士<br>國立臺灣大學<br>微生物與生化學研究所<br>96<br>Microbial photoreceptors play important roles when responding to environmental stimuli in microbes and a total of more than one hundred microbial photoreceptors has been identified. So far, four distinct functions have been identified, including bacteriorhodopsin (BR) that functions as outward proton pump, and halorhodopsin (HR) can serve as an inward chloride pump; sensory rhodopsin I (SRI) was shown to mediate both attract and repellent signaling, and sensory rhodopsin II (SRII) that triggers repellent signaling against near-UV light.
According to the genome project of the Haloarcula marismortui, there are six predicted photoreceptors, the most numbered photoreceptors in a single archaeon. This goals of this study will be: A) Determination of the maximum absorbance and functions of those six predicted photoreceptors and, B) the potential physiological roles of them.
We successful cloned and over-expressed all six photoreceptors using E. coli system, and concluded that: First, those six photoreceptors have their characteristic maximum absorbance, and second, a unique distribution pattern according to the maximum absorbance of those six photoreceptors. Third, a new type of sensory rhodopsin is identified; and fourth, two photoreceptors function as proton pumps and share the same maximum absorbance; the first time a single microbe found to have two light-driven proton pumps. Finally, chromatic adaptation is concluded in H. marismortui since different expression levels of HmSRM was observed when cells were grown under different wavelength of lights. Also, all of the photoreceptors were officially nominated after the confirmation of their characteristics as standard photoreceptors.
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Sue, Li-Chun, and 蘇莉鈞. "The Study on Recognizing Learning Emotion with the Features of Facial Expression." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/56568138331345709106.
Full textAbstract:
碩士<br>國立中興大學<br>資訊管理學系所<br>105<br>Facial expression is the ways of communication for the human, expressing their emotions by the facial expression. In the learning activities, the learners'' emotional expression will directly or indirectly affect their learning motivation and effectiveness. The change of emotions for the learners in the learning process is complex and diverse, and the emotions are mainly to explore the emotions about learning, which are: Cognitive Emotions and Academic Emotions. Cognitive emotions are complex. The label of Cognitive emotions not only can represent a variety of emotions for the single label but also is numerous names which are often discussed are: Confusion, Engaged and so on. Cognitive emotions are usually the short-term emotions. Hence, the academic emotions are referred to the learners’ feeling which through the cognitive assessment in the learning environment. It is integration and review of emotions for the learners in the leaning process. However, the domestic and foreign researches in recent years to obtain academic emotions are mostly filled "Achievement emotions questionnaire (AEQ)".
This study summarizes the Cognitive Emotions, which are emotions about learning and corresponds to Academic Emotions. In order to name a new set of emotions: Learning Emotion, which is a shorter duration of Academic Emotion. And the classes (or labels) of Learning Emotion are same as Academic Emotion: Enjoyment, Hope, Pride, Boredom, Anxiety and also have a Neutral expression.
The purpose of this study is to establish an effective learning emotions prediction model because we want to know the learners’ academic emotion without filling the questionnaire and by observing the sequence of learning emotion we can realize the leaners’ emotional change which caused by leaning activities in the learning process. Therefore, this study will establish the database about Learning Emotion, named as “Learning Emotion Face Image Database”, as the training data for the model. Under the image processing, two different facial features in the image are captured, which are “Feature Value” and “Action Units”. These facial features are input vectors for machine learning approaches as SVM, MLP and RF. The best performance of above-mentioned set: Feature Value and RF will be chose as our model’s method. Then through the process of feature selection and model parameter optimization, the accuracy rate of prediction model is over 87% for training data in 80/20 rule.
In this study, the results of prediction model: Learning Emotion States are compared with the outcome of AEQ: Academic Emotion States. The averaging correct rate of learning emotion states to academic emotion states is 60.47%. Nevertheless, after enlarging twice the latter part of learning emotion sequence, the highest correct rate is 77%. This shows that the latter part of the learning emotion sequence for detecting academic emotions is more influential than the previous part of learning emotion sequence. As the result of high correct rate, we verify our prediction model in this study has a certain degree of trustworthiness.
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Yuan, Wei-Shen, and 袁偉勝. "Modeling transcript expression by using chromatin features and transcription factor binding activity." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/26243449133855865069.
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碩士<br>國立中興大學<br>基因體暨生物資訊學研究所<br>99<br>Histone modification plays an important role in biology processes including transcriptional regulation, replication, alternative splicing, DNA repair, apoptosis, and gene silencing. Histone modification has been demonstrated to regulate gene transcription in positive or negative effect depending on the modification site and type. For example, H3K4me3 and H3K4me1 are correlated with gene activation, whereas H3K27me3 is negatively correlated with gene expression. Transcription factor is a protein that binds to specific DNA sequences in promoter region. The transcription factor can effect gene transcription by regulating the RNA polymerase binding with DNA template. To study the relationship between chromatin feature and transcription expression, we used Support Vector Regression (SVR) to build a model by using both chromatin features and transcription factor binding activity. Moreover, we found that the markers that close to the transcription start site are important for the transcript expression prediction. Using the upstream and downstream of first intron also helps the prediction of transcript expression.