Please use this identifier to cite or link to this item:
標題: Predicting human protein subcellular localization by heterogeneous and comprehensive approaches
作者: Tung, Chi-Hua
Chen, Chi-Wei
Sun, Han-Hao
Chu, Yen-Wei
關鍵字: Amino Acids;Humans;Subcellular Fractions
Project: PloS one, Volume 12, Issue 6, Page(s) e0178832.
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
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 SizeFormat Existing users please Login
pone.0178832.pdf期刊論文2.16 MBAdobe PDFThis file is only available in the university internal network   
Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.