Relevant bibliographies by topics / Subcellular localization prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Subcellular localization prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Subcellular localization prediction"

1

Lertampaiporn, Supatcha, Sirapop Nuannimnoi, Tayvich Vorapreeda, Nipa Chokesajjawatee, Wonnop Visessanguan, and Chinae Thammarongtham. "PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins." BioMed Research International 2019 (November 19, 2019): 1–11. http://dx.doi.org/10.1155/2019/5617153.

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Several computational approaches for predicting subcellular localization have been developed and proposed. These approaches provide diverse performance because of their different combinations of protein features, training datasets, training strategies, and computational machine learning algorithms. In some cases, these tools may yield inconsistent and conflicting prediction results. It is important to consider such conflicting or contradictory predictions from multiple prediction programs during protein annotation, especially in the case of a multiclass classification problem such as subcellular localization. Hence, to address this issue, this work proposes the use of the particle swarm optimization (PSO) algorithm to combine the prediction outputs from multiple different subcellular localization predictors with the aim of integrating diverse prediction models to enhance the final predictions. Herein, we present PSO-LocBact, a consensus classifier based on PSO that can be used to combine the strengths of several preexisting protein localization predictors specially designed for bacteria. Our experimental results indicate that the proposed method can resolve inconsistency problems in subcellular localization prediction for both Gram-negative and Gram-positive bacterial proteins. The average accuracy achieved on each test dataset is over 98%, higher than that achieved with any individual predictor.
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Han, Guo-Sheng, and Zu-Guo Yu. "ML-rRBF-ECOC: A Multi-Label Learning Classifier for Predicting Protein Subcellular Localization with Both Single and Multiple Sites." Current Proteomics 16, no. 5 (July 2, 2019): 359–65. http://dx.doi.org/10.2174/1570164616666190103143945.

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Background: The subcellular localization of a protein is closely related with its functions and interactions. More and more evidences show that proteins may simultaneously exist at, or move between, two or more different subcellular localizations. Therefore, predicting protein subcellular localization is an important but challenging problem. Observation: Most of the existing methods for predicting protein subcellular localization assume that a protein locates at a single site. Although a few methods have been proposed to deal with proteins with multiple sites, correlations between subcellular localization are not efficiently taken into account. In this paper, we propose an integrated method for predicting protein subcellular localizations with both single site and multiple sites. Methods: Firstly, we extend the Multi-Label Radial Basis Function (ML-RBF) method to the regularized version, and augment the first layer of ML-RBF to take local correlations between subcellular localization into account. Secondly, we embed the modified ML-RBF into a multi-label Error-Correcting Output Codes (ECOC) method in order to further consider the subcellular localization dependency. We name our method ML-rRBF-ECOC. Finally, the performance of ML-rRBF-ECOC is evaluated on three benchmark datasets. Results: The results demonstrate that ML-rRBF-ECOC has highly competitive performance to the related multi-label learning method and some state-of-the-art methods for predicting protein subcellular localizations with multiple sites. Considering dependency between subcellular localizations can contribute to the improvement of prediction performance. Conclusion: This also indicates that correlations between different subcellular localizations really exist. Our method at least plays a complementary role to existing methods for predicting protein subcellular localizations with multiple sites.
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Wu, Ze Yue, and Yue Hui Chen. "Predicting Protein Subcellular Localization Using the Algorithm of Diversity Finite Coefficient Combined with Artificial Neural Network." Advanced Materials Research 756-759 (September 2013): 3760–65. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3760.

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Protein subcellular localization is an important research field of bioinformatics. The subcellular localization of proteins classification problem is transformed into several two classification problems with error-correcting output codes. In this paper, we use the algorithm of the increment of diversity combined with artificial neural network to predict protein in SNL6 which has six subcelluar localizations. The prediction ability was evaluated by 5-jackknife cross-validation. Its predicted result is 81.3%. By com-paring its results with other methods, it indicates the new approach is feasible and effective.
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Yu, Chin-Sheng, Yu-Ching Chen, Chih-Hao Lu, and Jenn-Kang Hwang. "Prediction of protein subcellular localization." Proteins: Structure, Function, and Bioinformatics 64, no. 3 (June 2, 2006): 643–51. http://dx.doi.org/10.1002/prot.21018.

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Yang, Xiao-Fei, Yuan-Ke Zhou, Lin Zhang, Yang Gao, and Pu-Feng Du. "Predicting LncRNA Subcellular Localization Using Unbalanced Pseudo-k Nucleotide Compositions." Current Bioinformatics 15, no. 6 (November 11, 2020): 554–62. http://dx.doi.org/10.2174/1574893614666190902151038.

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Background: Long non-coding RNAs (lncRNAs) are transcripts with a length more than 200 nucleotides, functioning in the regulation of gene expression. More evidence has shown that the biological functions of lncRNAs are intimately related to their subcellular localizations. Therefore, it is very important to confirm the lncRNA subcellular localization. Methods: In this paper, we proposed a novel method to predict the subcellular localization of lncRNAs. To more comprehensively utilize lncRNA sequence information, we exploited both kmer nucleotide composition and sequence order correlated factors of lncRNA to formulate lncRNA sequences. Meanwhile, a feature selection technique which was based on the Analysis Of Variance (ANOVA) was applied to obtain the optimal feature subset. Finally, we used the support vector machine (SVM) to perform the prediction. Results: The AUC value of the proposed method can reach 0.9695, which indicated the proposed predictor is an efficient and reliable tool for determining lncRNA subcellular localization. Furthermore, the predictor can reach the maximum overall accuracy of 90.37% in leave-one-out cross validation, which clearly outperforms the existing state-of- the-art method. Conclusion: It is demonstrated that the proposed predictor is feasible and powerful for the prediction of lncRNA subcellular. To facilitate subsequent genetic sequence research, we shared the source code at https://github.com/NicoleYXF/lncRNA.
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Semwal, Rahul, and Pritish Kumar Varadwaj. "HumDLoc: Human Protein Subcellular Localization Prediction Using Deep Neural Network." Current Genomics 21, no. 7 (October 22, 2020): 546–57. http://dx.doi.org/10.2174/1389202921999200528160534.

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Aims: To develop a tool that can annotate subcellular localization of human proteins. Background: With the progression of high throughput human proteomics projects, an enormous amount of protein sequence data has been discovered in the recent past. All these raw sequence data require precise mapping and annotation for their respective biological role and functional attributes. The functional characteristics of protein molecules are highly dependent on the subcellular localization/ compartment. Therefore, a fully automated and reliable protein subcellular localization prediction system would be very useful for current proteomic research. Objective: To develop a machine learning-based predictive model that can annotate the subcellular localization of human proteins with high accuracy and precision. Methods: In this study, we used the PSI-CD-HIT homology criterion and utilized the sequence-based features of protein sequences to develop a powerful subcellular localization predictive model. The dataset used to train the HumDLoc model was extracted from a reliable data source, Uniprot knowledge base, which helps the model to generalize on the unseen dataset. Result : The proposed model, HumDLoc, was compared with two of the most widely used techniques: CELLO and DeepLoc, and other machine learning-based tools. The result demonstrated promising predictive performance of HumDLoc model based on various machine learning parameters such as accuracy (≥97.00%), precision (≥0.86), recall (≥0.89), MCC score (≥0.86), ROC curve (0.98 square unit), and precision-recall curve (0.93 square unit). Conclusion: In conclusion, HumDLoc was able to outperform several alternative tools for correctly predicting subcellular localization of human proteins. The HumDLoc has been hosted as a web-based tool at https://bioserver.iiita.ac.in/HumDLoc/.
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Li, Bo, Lijun Cai, Bo Liao, Xiangzheng Fu, Pingping Bing, and Jialiang Yang. "Prediction of Protein Subcellular Localization Based on Fusion of Multi-view Features." Molecules 24, no. 5 (March 6, 2019): 919. http://dx.doi.org/10.3390/molecules24050919.

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The prediction of protein subcellular localization is critical for inferring protein functions, gene regulations and protein-protein interactions. With the advances of high-throughput sequencing technologies and proteomic methods, the protein sequences of numerous yeasts have become publicly available, which enables us to computationally predict yeast protein subcellular localization. However, widely-used protein sequence representation techniques, such as amino acid composition and the Chou’s pseudo amino acid composition (PseAAC), are difficult in extracting adequate information about the interactions between residues and position distribution of each residue. Therefore, it is still urgent to develop novel sequence representations. In this study, we have presented two novel protein sequence representation techniques including Generalized Chaos Game Representation (GCGR) based on the frequency and distributions of the residues in the protein primary sequence, and novel statistics and information theory (NSI) reflecting local position information of the sequence. In the GCGR + NSI representation, a protein primary sequence is simply represented by a 5-dimensional feature vector, while other popular methods like PseAAC and dipeptide adopt features of more than hundreds of dimensions. In practice, the feature representation is highly efficient in predicting protein subcellular localization. Even without using machine learning-based classifiers, a simple model based on the feature vector can achieve prediction accuracies of 0.8825 and 0.7736 respectively for the CL317 and ZW225 datasets. To further evaluate the effectiveness of the proposed encoding schemes, we introduce a multi-view features-based method to combine the two above-mentioned features with other well-known features including PseAAC and dipeptide composition, and use support vector machine as the classifier to predict protein subcellular localization. This novel model achieves prediction accuracies of 0.927 and 0.871 respectively for the CL317 and ZW225 datasets, better than other existing methods in the jackknife tests. The results suggest that the GCGR and NSI features are useful complements to popular protein sequence representations in predicting yeast protein subcellular localization. Finally, we validate a few newly predicted protein subcellular localizations by evidences from some published articles in authority journals and books.
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Wang, Zhen, Quan Zou, Yi Jiang, Ying Ju, and Xiangxiang Zeng. "Review of Protein Subcellular Localization Prediction." Current Bioinformatics 9, no. 3 (February 11, 2014): 331–42. http://dx.doi.org/10.2174/1574893609666140212000304.

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Nair B J, Bipin, and Ashik P.v. "Plant and Animal sub cellular component localization prediction using multiple combination of various machine learning approaches." International Journal of Engineering & Technology 7, no. 1.9 (March 1, 2018): 221. http://dx.doi.org/10.14419/ijet.v7i1.9.9828.

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Membrane proteins are encoded in the genome and functionally important in the living organisms. Information on subcellular localization of cellular proteins has a significant role in the function of cell organelles. Discovery of drug target and system biology between localization and biological function are highly correlated. Therefore, we are predicting the localization of protein using various machine learning approaches. The prediction system based on the integration of the outcome of five sequence based sub-classifiers. The subcellular localization prediction of the final result is based on protein profile vector, which is a result of the sub-classifiers.
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YANG, YANG, and BAO-LIANG LU. "PROTEIN SUBCELLULAR MULTI-LOCALIZATION PREDICTION USING A MIN-MAX MODULAR SUPPORT VECTOR MACHINE." International Journal of Neural Systems 20, no. 01 (February 2010): 13–28. http://dx.doi.org/10.1142/s0129065710002206.

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Prediction of protein subcellular localization is an important issue in computational biology because it provides important clues for the characterization of protein functions. Currently, much research has been dedicated to developing automatic prediction tools. Most, however, focus on mono-locational proteins, i.e., they assume that proteins exist in only one location. It should be noted that many proteins bear multi-locational characteristics and carry out crucial functions in biological processes. This work aims to develop a general pattern classifier for predicting multiple subcellular locations of proteins. We use an ensemble classifier, called the min-max modular support vector machine (M3-SVM), to solve protein subcellular multi-localization problems; and, propose a module decomposition method based on gene ontology (GO) semantic information for M3-SVM. The amino acid composition with secondary structure and solvent accessibility information is adopted to represent features of protein sequences. We apply our method to two multi-locational protein data sets. The M3-SVMs show higher accuracy and efficiency than traditional SVMs using the same feature vectors. And the GO decomposition also helps to improve prediction accuracy. Moreover, our method has a much higher rate of accuracy than existing subcellular localization predictors in predicting protein multi-localization.
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Dissertations / Theses on the topic "Subcellular localization prediction"

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Ozarar, Mert. "Prediction Of Protein Subcellular Localization Based On Primary Sequence Data." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1082320/index.pdf.

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Subcellular localization is crucial for determining the functions of proteins. A system called prediction of protein subcellular localization (P2SL) that predicts the subcellular localization of proteins in eukaryotic organisms based on the amino acid content of primary sequences using amino acid order is designed. The approach for prediction is to nd the most frequent motifs for each protein in a given class based on clustering via self organizing maps and then to use these most frequent motifs as features for classication by the help of multi layer perceptrons. This approach allows a classication independent of the length of the sequence. In addition to these, the use of a new encoding scheme is described for the amino acids that conserves biological function based on point of accepted mutations (PAM) substitution matrix. The statistical test results of the system is presented on a four class problem. P2SL achieves slightly higher prediction accuracy than the similar studies.
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Bozkurt, Burcin. "Prediction Of Protein Subcellular Localization Using Global Protein Sequence Feature." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/3/1135292/index.pdf.

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The problem of identifying genes in eukaryotic genomic sequences by computational methods has attracted considerable research attention in recent years. Many early approaches to the problem focused on prediction of individual functional elements and compositional properties of coding and non coding deoxyribonucleic acid (DNA) in entire eukaryotic gene structures. More recently, a number of approaches has been developed which integrate multiple types of information including structure, function and genetic properties of proteins. Knowledge of the structure of a protein is essential for describing and understanding its function. In addition, subcellular localization of a protein can be used to provide some amount of characterization of a protein. In this study, a method for the prediction of protein subcellular localization based on primary sequence data is described. Primary sequence data for a protein is based on amino acid sequence. The frequency value for each amino acid is computed in one given position. Assigned frequencies are used in a new encoding scheme that conserves biological information based on point accepted mutations (PAM) substitution matrix. This method can be used to predict the nuclear, the cytosolic sequences, the mitochondrial targeting peptides (mTP) and the signal peptides (SP). For clustering purposes, other than well known traditional techniques, principle component analysis (PCA)"
and self-organizing maps (SOM)"
are used. For classication purposes, support vector machines (SVM)"
, a method of statistical learning theory recently introduced to bioinformatics is used. The aim of the combination of feature extraction, clustering and classification methods is to design an acccurate system that predicts the subcellular localization of proteins presented into the system. Our scheme for combining several methods is cascading or serial combination according to its architecture. In the cascading architecture, the output of a method serves as the input of the other model used.
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Scott, Michelle. "Protein subcellular localization : analysis and prediction using the endoplasmic reticulum as a model organelle." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102170.

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Eukaryotic cells are divided into subcellular organelles that generate appropriate molecular environments for the functions they harbour. As such, subcellular localization is a key characteristic that provides valuable clues regarding protein function and, when studied globally, a better understanding of cellular processes. The organelles of the secretory pathway are responsible for the processing of all proteins destined for secretion, the plasma membrane as well as their own resident proteins. This group of organelles is difficult to study experimentally because they are difficult to purify to homogeneity.
To facilitate the investigation of the endoplasmic reticulum (ER) and more generally, the secretory pathway, we have created Hera, a publicly accessible protein localization database. Originally designed to house characteristics of ER proteins, it currently contains tens of thousands of proteins from different organisms and subcellular compartments. Hera was originally used to investigate features of ER proteins, providing insight into the extent of usage of various localization mechanisms, including both well-studied but also non-classical and novel mechanisms.
Hera was subsequently used to create Bayesian network type localization predictors. By considering the combinatorial presence of motifs, domains, targeting signals and using in some cases, protein interaction information, our predictors achieve high accuracy and coverage. When our predictions are compared with localization annotations from high-throughput studies in both human and yeast, we find that disagreements mainly involve proteins in the secretory pathway. Our predictors can be used to independently validate these large-scale studies. We further refined the localization prediction of the whole yeast proteome by distinguishing proteins localized to the lumen or membrane of various organelles from cytosolic proteins peripherally associated with these organelles.
Hera was also used to investigate efficient and informative approaches to interrogate interaction networks in order to gain insight into the relationship between proteins/genes of interest. By combining interaction and refined localization information, we constructed localizome-interactome networks of whole organelles. Such models provide insight into global organellar characteristics and inter-organellar mechanisms of communication.
The research presented in this thesis demonstrates that the integration, in an appropriate framework such as Bayesian networks, of widely available information such as localization and interaction data allows to gain deep insights into cellular processes.
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Zhu, Lu [Verfasser]. "Context-specific subcellular localization prediction: Leveraging protein interaction networks and scientific texts / Lu Zhu." Bielefeld : Universitätsbibliothek Bielefeld, 2018. http://d-nb.info/1169314589/34.

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Fagerberg, Linn. "Mapping the human proteome using bioinformatic methods." Doctoral thesis, KTH, Proteomik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-31477.

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The fundamental goal of proteomics is to gain an understanding of the expression and function of the proteome on the level of individual proteins, on the level of defined cell types and on the level of the entire organism. In this thesis, the human proteome is explored using membrane protein topology prediction methods to define the human membrane proteome and by global protein expression profiling, which relies on a complex study of the location and expression levels of proteins in tissues and cells. A whole-proteome analysis was performed based on the predicted protein-coding genes of humans using a selection of membrane protein topology prediction methods. The study used a majority decision-based method, which estimated that approximately 26% of the human genes encode for a membrane protein. The prediction results are displayed in a visualization tool to facilitate the selection of antigens to be used for antibody generation. Global protein expression profiles in a large number of cells and tissues in the human body were analyzed for more than 4000 protein targets, based on data from the antibody-based immunohistochemistry and immunofluorescence methods within the framework of the Human Protein Atlas project. The results revealed few cell-type specific proteins and a high fraction of human proteins expressed in most cells, suggesting that cell and tissue specificity is attained by a fine-tuned regulation of protein levels. The expression profiles were also used to analyze the relationship between 45 cell lines by hierarchical clustering and principal component analysis. The global protein expression patterns overall reflected the tumor origin of the cells, and also allowed for identification of proteins of importance for distinguishing different categories of cell lines, as defined by phenotype of progenitor cell. In addition, the protein distribution in 16 subcellular compartments in three of the human cell lines was mapped. A large fraction of proteins were localized in two or more compartments and, in line with previous results, a majority of proteins were detected in all three cell lines. Finally, mass spectrometry-based protein expression levels were compared to RNA-seq-based transcript expression levels in three cell lines. Highly ubiquitous mRNA expression was found and the changes of expression levels between the cell lines showed high correlations between proteins and transcripts. Large general differences in abundance of proteins from various functional classes were observed. A comparison between categories based on expression levels revealed that, in general, genes with varying expression levels between the cell lines or only expressed in one cell line were highly enriched for cell-surface proteins. These studies show a path for a systematic analysis to characterize the proteome in human cells, tissues and organs.
QC 20110317
The Human Protein Atlas project
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Yu, Chin-Sheng, and 游景盛. "Prediction of Protein Subcellular Localization." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/28216444510128135886.

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博士
國立交通大學
生物科技系所
95
Since the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful to biologists to infer protein function. Recent years we have seen a surging interest in the development of novel computational tools to predict subcellular localization. With the rapid increase of sequenced genomic data, the need for an automated and accurate tool to predict subcellular localization becomes increasingly important. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. In this thesis, I used support vector machine (SVM) method based on n–peptide composition in predicting the subcellular locations of proteins. For an unbiased assessment of the results, we apply our approach to several independent data sets in the beginning. In those data sets, our approach gives superior performance compared with other approaches. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Rost and Nair (Protein Sci, 11:2836-47 (2002)) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization and found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences – some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we developed an approach based on a two-level SVM system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two iii often-used benchmark data sets – one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to check the relationship between sequence homology and localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs surprisingly well for sequences sharing homology as low as 30%, but its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will obviously lead to biased assessment of the performances of the predictive approaches - especially those relying on homology search or sequence annotations. Since our two-level classification system based on SVM does not rely on homology search, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach outperformed other existing approaches, even though some of which use homology search as part of their algorithms. Furthermore, for the practical purpose, we also develop a practical hybrid method that pipelines the two-level SVM classifier and the homology search method in sequential order as a general tool for the sequence annotation of subcellular localization. Our approaches should be valuable in the high throughput analysis of genomics and proteomics.
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Syu, Shiao-shan, and 徐筱姍. "Human Protein Subcellular Localization Prediction." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/96176482574886082780.

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碩士
逢甲大學
生醫資訊暨生醫工程碩士學位學程
99
The biological function of a protein in a cell is often closely correlated with its subcellular localization. Hence, the information about where a protein localized often offers important clues toward knowing the function of an uncharacterized sequence. The protein subcellular localization can be used as an important feature to screen for drug candidates, vaccine design, and gene products annotation. Here, We applied the support vector machine algorithm to a benchmark dataset of human protein sequence based on n-peptide composition. The first step of this method is that we classify the protein sequence by different feature then use SVM to predict subcellular localization. The second step, we use the result of the first step to predict again by the support vector machine classifier.We use PSLT training Hera data set, this data set is include 2233 human protein sequence and 9 subcellular localizations inside of cell. Our method achieves an overall classification accuracy of 80% as estimated by using a 10-fold cross-validation test with coverage of 74%. For the rest 26%, our method achieves an overall classification the accuracy of 45%. This research should provide an important tool in human genomics and proteomics studies.
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Chen, Shu-Pin, and 陳書品. "Prediction of eukaryotic protein subcellular localization." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/10932435428409959975.

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碩士
國立中央大學
資訊工程研究所
96
Prediction of subcellular localization of various proteins is an important and well-studied problem. Each compartment in cell has specific tasks, and proteins in each compartment are synthesized to fulfill these tasks. Proteins localized in the same compartment are thought to have the same or similar function. Knowledge of the subcellular localization of a protein can significantly improve target identification during the drug discovery process. Current available methods extract information from amino acid sequence or signal peptide and lack more biological features like post-translational modification. We develop an integrated system for biologists to know which localization the proteins from eukaryote is located to. The system is based on protein sequence composition, signal peptide, protein domains from Pfam and homologs search.
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Chen, Shih-Hao, and 陳世豪. "Subcellular Localization Prediction of Eukaryotic Protein." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/80756466635576715069.

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碩士
臺中健康暨管理學院
生物資訊研究所
92
Biologically, the function of a protein is highly related to its subcellular localization. Accordingly, it is necessary to develop an automatic yet reliable method for protein subcellular localization prediction, especially when large-scale genome sequences are to be analyzed. Various methods have been proposed to perform the task. The results, however, are not satisfactory in terms of effectiveness and efficiency. In this paper, the proposed Bayesian inference method and The Information Gain used to observed important information, Moreover, the Nearest Neighbor Classification is considerably effective for subcellular localization prediction in a supervised fashion.
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Chen, Yu-Tzu, and 陳佑慈. "Protein-protein interaction prediction enhancement using subcellular localization." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/81806002826018277394.

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碩士
國立中央大學
資訊工程研究所
98
Protein–protein interactions are importance for almost every process in living cell. Abnormal interactions may have implications in a number of neurological syndromes. Therefore, it is crucial to recognize the association and dissociation of protein molecules. Current available computational methods of prediction of protein–protein interaction extract information from amino acid sequence or signal peptide. There are few method consider subcellular localization information. The method presented in this paper is based on the assumption that two proteins should appear on same subcellular localization to perform interaction. We develop an integrated system which based on a learning algorithm-support vector machine to predict protein–protein interactions. We construct training models for different subcellular localization. Each test protein pair request one training model to predict according to its localization. This method is take protein sequence composition, protein domains and subcellular localization information as features. The prediction ability of our method is better than other sequence-based protein–protein interaction prediction methods. In addition, a more complete data of protein-protein interactions and subcellular localizations can enhance the prediction ability of the method.
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Books on the topic "Subcellular localization prediction"

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author, Mak M. W., ed. Machine learning for protein subcellular localization prediction. Boston: De Gruyter, 2015.

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Book chapters on the topic "Subcellular localization prediction"

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Barberis, Elettra, Emilio Marengo, and Marcello Manfredi. "Protein Subcellular Localization Prediction." In Methods in Molecular Biology, 197–212. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1641-3_12.

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Nakai, Kenta, and Paul Horton. "Computational Prediction of Subcellular Localization." In Protein Targeting Protocols, 429–66. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-466-7_29.

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Gao, Qing-Bin, and Zheng-Zhi Wang. "Feature Subset Selection for Protein Subcellular Localization Prediction." In Computational Intelligence and Bioinformatics, 433–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11816102_47.

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Nair, Rajesh, and Burkhard Rost. "Protein Subcellular Localization Prediction Using Artificial Intelligence Technology." In Functional Proteomics, 435–63. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-398-1_27.

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Pan, Gaofeng, Chao Sun, Zijun Liao, and Jijun Tang. "Machine and Deep for Prediction of Subcellular Localization." In Methods in Molecular Biology, 249–61. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1641-3_15.

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Govindan, Geetha, and Achuthsankar S. Nair. "New Feature Vector for Apoptosis Protein Subcellular Localization Prediction." In Advances in Computing and Communications, 294–301. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22709-7_30.

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Özarar, Mert, Volkan Atalay, and Rengül Çetin Atalay. "Prediction of Protein Subcellular Localization Based on Primary Sequence Data." In Computer and Information Sciences - ISCIS 2003, 611–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39737-3_76.

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Jin, Bo, Yuchun Tang, Yan-Qing Zhang, Chung-Dar Lu, and Irene Weber. "The Binary Multi-SVM Voting System for Protein Subcellular Localization Prediction." In Computational Science and Its Applications – ICCSA 2005, 299–308. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424857_33.

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Mooney, Catherine, Yong-Hong Wang, and Gianluca Pollastri. "De Novo Protein Subcellular Localization Prediction by N-to-1 Neural Networks." In Computational Intelligence Methods for Bioinformatics and Biostatistics, 31–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21946-7_3.

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Wang, Lei, Dong Wang, Yaou Zhao, and Yuehui Chen. "Prediction of Subcellular Localization of Multi-site Virus Proteins Based on Convolutional Neural Networks." In Intelligent Computing Theories and Application, 606–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63312-1_53.

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Conference papers on the topic "Subcellular localization prediction"

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Juan, Eric Y. T., J. H. Chang, C. H. Li, and B. Y. Chen. "Methods for Protein Subcellular Localization Prediction." In 2011 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS). IEEE, 2011. http://dx.doi.org/10.1109/cisis.2011.91.

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HORTON, PAUL, KEUN-JOON PARK, TAKESHI OBAYASHI, and KENTA NAKAI. "PROTEIN SUBCELLULAR LOCALIZATION PREDICTION WITH WOLF PSORT." In 4th Asia-Pacific Bioinformatics Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2005. http://dx.doi.org/10.1142/9781860947292_0007.

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Zhao, Qing, Na Li, and Li Fang. "Prediction of Multi-site Protein Subcellular Localization." In 2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). IEEE, 2020. http://dx.doi.org/10.1109/tocs50858.2020.9339688.

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Upama, Paramita Basak, Shahin Akhter, and Mohammad Imam Hasan Bin Asad. "Prediction of Protein Subcellular Localization using Machine Learning." In 2018 4th International Conference for Convergence in Technology (I2CT). IEEE, 2018. http://dx.doi.org/10.1109/i2ct42659.2018.9057828.

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AßFALG, JOHANNES, JING GONG, HANS-PETER KRIEGEL, ALEXEY PRYAKHIN, TIANDI WEI, and ARTHUR ZIMEK. "SUPERVISED ENSEMBLES OF PREDICTION METHODS FOR SUBCELLULAR LOCALIZATION." In The 6th Asia-Pacific Bioinformatics Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2007. http://dx.doi.org/10.1142/9781848161092_0006.

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Zhang, Shu-Bo. "A Hybrid System for Prediction of Protein Subcellular Localization." In 2009 2nd International Conference on Biomedical Engineering and Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bmei.2009.5305500.

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Zidoum, Hamza, and Mennatollah Magdy. "Protein Subcellular and Secreted Localization Prediction Using Deep Learning." In 2018 International Conference on Computing Sciences and Engineering (ICCSE). IEEE, 2018. http://dx.doi.org/10.1109/iccse1.2018.8374220.

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Mondal, Ananda Mohan, Jhih-rong Lin, Jianjun Hu, and Ananda Mohan Mondal. "Network based subcellular localization prediction for multi-label proteins." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112416.

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Yuan Lan, Yeng Chai Soh, and Guang-Bin Huang. "Extreme Learning Machine based bacterial protein subcellular localization prediction." In 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008 - Hong Kong). IEEE, 2008. http://dx.doi.org/10.1109/ijcnn.2008.4634051.

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Liu, Xiyu. "Comprehensive Prediction and Interpretation of Viral Protein Subcellular Localization." In ICBBE '19: 2019 6th International Conference on Biomedical and Bioinformatics Engineering. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3375923.3375950.

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