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
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
Appears in Collections:基因體暨生物資訊學研究所

文件中的檔案:

取得全文請前往華藝線上圖書館



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