Please use this identifier to cite or link to this item:
http://hdl.handle.net/11455/95844
標題: | Predicting human protein subcellular localization by heterogeneous and comprehensive approaches | 作者: | Tung, Chi-Hua Chen, Chi-Wei Sun, Han-Hao 朱彥煒 Chu, Yen-Wei |
關鍵字: | Amino Acids;Humans;Subcellular Fractions | 出版社: | PLOS ONE | Project: | PloS one, Volume 12, Issue 6, Page(s) e0178832. | 摘要: | Drug development and investigation of protein function both require an understanding of protein subcellular localization. We developed a system, REALoc, that can predict the subcellular localization of singleplex and multiplex proteins in humans. This system, based on comprehensive strategy, consists of two heterogeneous systematic frameworks that integrate one-to-one and many-to-many machine learning methods and use sequence-based features, including amino acid composition, surface accessibility, weighted sign aa index, and sequence similarity profile, as well as gene ontology function-based features. REALoc can be used to predict localization to six subcellular compartments (cell membrane, cytoplasm, endoplasmic reticulum/Golgi, mitochondrion, nucleus, and extracellular). REALoc yielded a 75.3% absolute true success rate during five-fold cross-validation and a 57.1% absolute true success rate in an independent database test, which was >10% higher than six other prediction systems. Lastly, we analyzed the effects of Vote and GANN models on singleplex and multiplex localization prediction efficacy. REALoc is freely available at http://predictor.nchu.edu.tw/REALoc. |
URI: | http://hdl.handle.net/11455/95844 | DOI: | 10.1371/journal.pone.0178832 |
Appears in Collections: | 基因體暨生物資訊學研究所 |
Files in This Item:
File | Description | Size | Format | Existing users please Login |
---|---|---|---|---|
pone.0178832.pdf | 期刊論文 | 2.16 MB | Adobe PDF | This file is only available in the university internal network |
TAIR Related Article
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.