title: HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.
abstract: Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.
authors.affiliation: Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China.
authors.forename: Shibiao
authors.initials: S
authors.lastname: Wan
authors.affiliation: Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China.
authors.forename: Man-Wai
authors.initials: MW
authors.lastname: Mak
authors.affiliation: Department of Electrical Engineering, Princeton University, Princeton, New Jersey, United States of America.
authors.forename: Sun-Yuan
authors.initials: SY
authors.lastname: Kung
mesh_terms: D019295:Computational Biology; D030562:Databases, Protein; D063990:Gene Ontology / Q000706:statistics & numerical data*; D058977:Molecular Sequence Annotation; D059828:Plant Cells / Q000737:chemistry / Q000821:virology; D010940:Plant Proteins / Q000032:analysis*; D010944:Plants / Q000737:chemistry; D020539:Sequence Analysis, Protein; D012984:Software; D060388:Support Vector Machine; D014764:Viral Proteins / Q000032:analysis*; D014780:Viruses / Q000737:chemistry; D018875:Vocabulary, Controlled
chemical_list: D010940:Plant Proteins; D014764:Viral Proteins