Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/95844
DC FieldValueLanguage
dc.contributor.authorTung, Chi-Huazh_TW
dc.contributor.authorChen, Chi-Weizh_TW
dc.contributor.authorSun, Han-Haozh_TW
dc.contributor.author朱彥煒zh_TW
dc.contributor.authorChu, Yen-Weizh_TW
dc.date2017-06-28-
dc.date.accessioned2018-10-26T07:11:22Z-
dc.date.available2018-10-26T07:11:22Z-
dc.identifier.urihttp://hdl.handle.net/11455/95844-
dc.description.abstractDrug 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.zh_TW
dc.language.isoen_USzh_TW
dc.publisherPLOS ONEzh_TW
dc.relationPloS one, Volume 12, Issue 6, Page(s) e0178832.zh_TW
dc.subjectAmino Acidszh_TW
dc.subjectHumanszh_TW
dc.subjectSubcellular Fractionszh_TW
dc.titlePredicting human protein subcellular localization by heterogeneous and comprehensive approacheszh_TW
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1371/journal.pone.0178832zh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeJournal Article-
item.cerifentitytypePublications-
item.fulltextwith fulltext-
item.languageiso639-1en_US-
item.grantfulltextrestricted-
Appears in Collections:基因體暨生物資訊學研究所
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