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Thematische Bibliographien / Subcellular localization prediction / Dissertationen

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Dissertationen zum Thema „Subcellular localization prediction“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 15. Februar 2022

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1

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 classic

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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 describi

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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

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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

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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<br>國立交通大學<br>生物科技系所<br>95<br>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 alg

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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<br>逢甲大學<br>生醫資訊暨生醫工程碩士學位學程<br>99<br>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

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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<br>國立中央大學<br>資訊工程研究所<br>96<br>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 fea

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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<br>臺中健康暨管理學院<br>生物資訊研究所<br>92<br>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 Neig

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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<br>國立中央大學<br>資訊工程研究所<br>98<br>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 ap

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Su, Chia-Yu, and 蘇家玉. "Prediction of Subcellular Localization and RNA-binding Sites in Proteins." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/43820761805319359116.

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博士<br>國立交通大學<br>生物資訊研究所<br>97<br>Automated function annotation is a major goal of post-genomic era with tremendous amount of protein sequences in the databases. Prediction of subcellular localization or binding sites in proteins is crucial for function analysis, genome annotation, and drug discovery. Determination of localization or structure using experimental approaches is time-consuming; thus, computational approaches become highly desirable. We proposed two protein subcellular localization prediction methods, PSL101 and PSLDoc. PSL101 combines a structural homology approach and a suppo

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Gaston, Daniel. "PHYLOGENOMIC APPROACHES TO THE ANALYSIS OF FUNCTIONAL DIVERGENCE AND SUBCELLULAR LOCALIZATION." 2012. http://hdl.handle.net/10222/14439.

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With rapid advances in sequencing technologies and precipitous decreases in cost, public sequence databases have increased in size apace. However, experimental characterization of novel genes and their products remains prohibitively expensive and time consuming. For these reasons, bioinformatics approaches have become increasingly necessary to generate hypotheses of biological function. Phylogenomic approaches use phylogenetic methods to place genes, chromosomes, or whole genomes within the context of their evolutionary history and can be used to predict the function of encoded proteins. In th

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Yen, Shou-Cheng, and 嚴守正. "Towards Improving Accuracy of Subcellular Localization Prediction for Lysosomal, Peroxisomal and ER Proteins." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/22770069206338505923.

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碩士<br>國立陽明大學<br>生物醫學資訊研究所<br>99<br>Within a cell biological functions are often localized in specific subcellular compartments. Hence the ability to predicting the subcellular localizations for uncharacterized proteins is critical for protein functional annotation. This study describes a novel method for identifying sequence motifs to predict protein subcellular localizations. Existing methods mostly rely on prior knowledge about protein targeting signals and sophisticated residue compositions that provide obscure insights about cellular functions. Here we proposed a systematic approach to ide

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Huang, Wen-Lin, and 黃文玲. "Using Gene Ontology Annotation and Physicochemical Properties for Prediction of Protein Subcellular and Subnuclear Localization." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/09303799954362845008.

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博士<br>逢甲大學<br>資訊工程所<br>96<br>Eukaryotic cells consist of some major parts, the nucleus, cytoplasm, Mitochondrion, Extracellular, and Chloroplast. One of the fundamental goals in molecular cell biology and proteomics is to identify their subcellular locations or environments because the function of a protein and its role in a cell are closely correlated with which compartment or organelle it resides in. The knowledge thus obtained can help us timely utilize these newly found protein sequences for both basic research and drug discovery. Among the subcellualr compartments, the nucleus is a highly

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Liao, Jun-Qin, and 廖俊欽. "Protein Subcellular Localization Prediction by Support Vector Machine and Genetic Algorithm based on n-Peptide Compositions." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/4u3ruf.

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Li, Wei-Jyun, and 李瑋峻. "Predicting Protein Subcellular Localization Using Integrative System." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/46151991856883970059.

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碩士<br>國立臺灣海洋大學<br>資訊工程學系<br>97<br>The prediction of protein subcellular localization (PSL) has become a popular field in recent years because it can help protein function prediction and genome annotation, and thus aid the drug design. However, the experimental methods for analyzing PSL are often expensive and time-consuming tasks. Therefore, the computational prediction of PSL, with the use of information in databases, has become a vibrant field of study. Nevertheless, it is still a tough task to extract suitable features from proteins for accurate prediction of PSL due to the complex structur

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Nathan, Michel. "A multiple site predictor for subcellular localization of fungal proteins." Thesis, 2006. http://spectrum.library.concordia.ca/9050/1/MR20780.pdf.

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In this work, we build a system that uses a decision tree to predict fungal protein localization based on physiochemical properties of proteins calculable from their primary sequences. The training examples that serve as basis for learning are obtained from experimentally validated localizations. Although there is clear evidence of presence of the same protein in more than one sub-cellular compartment, almost all existing automated systems restrict their predictions to single-site localization. Here, we attempt to address this issue and for proteins that are reported to target more than one su

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LIN, TSAI-YU, and 林采妤. "Improvement of Predicting Human Protein Subcellular Localization Through Integrated Machine Learning Methods." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/5949gw.

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碩士<br>逢甲大學<br>資訊工程學系<br>106<br>The prediction of protein subcellular locations is an important topic in computational biology research over the past decade. Knowing protein subcellular localization can understand protein function as well as protein-protein interactions. However, relying on experimental methods to identify subcellular locations of proteins is often laborious and expensive, so when using large-scale protein datasets with unknown locations, it is highly desirable to use more efficient computer prediction tools. So far, many methods have been proposed to predict the location of la

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Sun, Han-Hao, and 孫翰豪. "REALoc: Reliable and effective methods to assist predicting human protein subcellular localization." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/76013331557236563304.

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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>101<br>Protein subcellular localization is an important part of biological research; which could support drug development and explore the function of proteins. Many subcellular localization prediction tools has developed, most of them used the data of eukaryotes or prokaryotes for model training, however, the related predictors for human proteins are rare. We established a system to predict subcellular localization of human proteins with Singleplex and Multiplex, called REALoc. It based on two layers architecture integrated with two different machine learning me

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Shen, Yaoqing. "In silico analysis of mitochondrial proteins." Thèse, 2009. http://hdl.handle.net/1866/3766.

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Le rôle important joué par la mitochondrie dans la cellule eucaryote est admis depuis longtemps. Cependant, la composition exacte des mitochondries, ainsi que les processus biologiques qui sy déroulent restent encore largement inconnus. Deux facteurs principaux permettent dexpliquer pourquoi létude des mitochondries progresse si lentement : le manque defficacité des méthodes didentification des protéines mitochondriales et le manque de précision dans lannotation de ces protéines. En conséquence, nous avons développé un nouvel outil informatique, YimLoc, qui permet de prédire avec succès les pr

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