Thematische Bibliographien / Subcellular localization prediction

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Auswahl der wissenschaftlichen Literatur zum Thema „Subcellular localization prediction“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 15. Februar 2022

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Zeitschriftenartikel zum Thema "Subcellular localization prediction"

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 subcellul

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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 (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

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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

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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 (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 (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 factor

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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 (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

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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 (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 in

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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 (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 (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 o

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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 (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 loc

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Dissertationen zum Thema "Subcellular localization prediction"

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 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 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 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

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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 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 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 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 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 ap

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Bücher zum Thema "Subcellular localization prediction"

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

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Buchteile zum Thema "Subcellular localization prediction"

Barberis, Elettra, Emilio Marengo, and Marcello Manfredi. "Protein Subcellular Localization Prediction." In Methods in Molecular Biology. 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. 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. 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. 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. 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. 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. 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. 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. 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. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63312-1_53.

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Konferenzberichte zum Thema "Subcellular localization prediction"

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. ACM, 2019. http://dx.doi.org/10.1145/3375923.3375950.

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