Literature DB >> 14623335

A new hybrid approach to predict subcellular localization of proteins by incorporating gene ontology.

Kuo-Chen Chou1, Yu-Dong Cai.   

Abstract

Based on the recent development in the gene ontology and functional domain databases, a new hybridization approach is developed for predicting protein subcellular location by combining the gene product, functional domain, and quasi-sequence-order effects. As a showcase, the same prokaryotic and eukaryotic datasets, which were studied by many previous investigators, are used for demonstration. The overall success rate by the jackknife test for the prokaryotic set is 94.7% and that for the eukaryotic set 92.9%. These are so far the highest success rates achieved for the two datasets by following a rigorous cross-validation test procedure, suggesting that such a hybrid approach may become a very useful high-throughput tool in the area of bioinformatics, proteomics, as well as molecular cell biology. The very high success rates also reflect the fact that the subcellular localization of a protein is closely correlated with: (1). the biological objective to which the gene or gene product contributes, (2). the biochemical activity of a gene product, and (3). the place in the cell where a gene product is active.

Mesh:

Substances:

Year:  2003        PMID: 14623335     DOI: 10.1016/j.bbrc.2003.10.062

Source DB:  PubMed          Journal:  Biochem Biophys Res Commun        ISSN: 0006-291X            Impact factor:   3.575


  27 in total

1.  Predicting subcellular localization via protein motif co-occurrence.

Authors:  Michelle S Scott; David Y Thomas; Michael T Hallett
Journal:  Genome Res       Date:  2004-10       Impact factor: 9.043

2.  Go molecular function terms are predictive of subcellular localization.

Authors:  Z Lu; L Hunter
Journal:  Pac Symp Biocomput       Date:  2005

3.  A novel representation of protein sequences for prediction of subcellular location using support vector machines.

Authors:  Setsuro Matsuda; Jean-Philippe Vert; Hiroto Saigo; Nobuhisa Ueda; Hiroyuki Toh; Tatsuya Akutsu
Journal:  Protein Sci       Date:  2005-11       Impact factor: 6.725

4.  Going from where to why--interpretable prediction of protein subcellular localization.

Authors:  Sebastian Briesemeister; Jörg Rahnenführer; Oliver Kohlbacher
Journal:  Bioinformatics       Date:  2010-03-17       Impact factor: 6.937

5.  Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks.

Authors:  Tao Huang; Xiao-He Shi; Ping Wang; Zhisong He; Kai-Yan Feng; Lele Hu; Xiangyin Kong; Yi-Xue Li; Yu-Dong Cai; Kuo-Chen Chou
Journal:  PLoS One       Date:  2010-06-04       Impact factor: 3.240

6.  YLoc--an interpretable web server for predicting subcellular localization.

Authors:  Sebastian Briesemeister; Jörg Rahnenführer; Oliver Kohlbacher
Journal:  Nucleic Acids Res       Date:  2010-05-27       Impact factor: 16.971

7.  Multi-label multi-kernel transfer learning for human protein subcellular localization.

Authors:  Suyu Mei
Journal:  PLoS One       Date:  2012-06-13       Impact factor: 3.240

8.  Gene ontology based transfer learning for protein subcellular localization.

Authors:  Suyu Mei; Wang Fei; Shuigeng Zhou
Journal:  BMC Bioinformatics       Date:  2011-02-02       Impact factor: 3.169

Review 9.  Computational and experimental approaches to chart the Escherichia coli cell-envelope-associated proteome and interactome.

Authors:  Juan Javier Díaz-Mejía; Mohan Babu; Andrew Emili
Journal:  FEMS Microbiol Rev       Date:  2008-11-27       Impact factor: 16.408

10.  MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction.

Authors:  Torsten Blum; Sebastian Briesemeister; Oliver Kohlbacher
Journal:  BMC Bioinformatics       Date:  2009-09-01       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.