swMATH ID: 26863
Software Authors: Chou KC, Shen HB
Description: Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization. Predicting subcellular localization of human proteins is a challenging problem, especially when unknown query proteins do not have significant homology to proteins of known subcellular locations and when more locations need to be covered. To tackle the challenge, protein samples are expressed by hybridizing the gene ontology (GO) database and amphiphilic pseudo amino acid composition (PseAA). Based on such a representation frame, a novel ensemble classifier, called ”Hum-PLoc”, was developed by fusing many basic individual classifiers through a voting system. The ”engine” of these basic classifiers was operated by the KNN (K-nearest neighbor) rule. As a demonstration, tests were performed with the ensemble classifier for human proteins among the following 12 locations: (1) centriole; (2) cytoplasm; (3) cytoskeleton; (4) endoplasmic reticulum; (5) extracell; (6) Golgi apparatus; (7) lysosome; (8) microsome; (9) mitochondrion; (10) nucleus; (11) peroxisome; (12) plasma membrane. To get rid of redundancy and homology bias, none of the proteins investigated here had > or = 25
Homepage: https://www.ncbi.nlm.nih.gov/pubmed/16808903
Related Software: Cell-PLoc; Euk-mPLoc; Euk-PLoc; Hum-mPLoc; Memtype-2L; LIBSVM; Gpos-PLoc; Virus-ploc; Plant-mPLoc; PseAAC; Signal-CF; iLoc-Euk; ProtIdent; EzyPred; Gneg-mPLoc; SecretP; GPCR-2L; MemHyb; PseAAC-General; BLAST
Cited in: 22 Publications

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