title: Mem-ADSVM: A two-layer multi-label predictor for identifying multi-functional types of membrane proteins.
abstract: Identifying membrane proteins and their multi-functional types is an indispensable yet challenging topic in proteomics and bioinformatics. However, most of the existing membrane-protein predictors have the following problems: (1) they do not predict whether a given protein is a membrane protein or not; (2) they are limited to predicting membrane proteins with single-label functional types but ignore those with multi-functional types; and (3) there is still much room for improvement for their performance. To address these problems, this paper proposes a two-layer multi-label predictor, namely Mem-ADSVM, which can identify membrane proteins (Layer I) and their multi-functional types (Layer II). Specifically, given a query protein, its associated gene ontology (GO) information is retrieved by searching a compact GO-term database with its homologous accession number. Subsequently, the GO information is classified by a binary support vector machine (SVM) classifier to determine whether it is a membrane protein or not. If yes, it will be further classified by a multi-label multi-class SVM classifier equipped with an adaptive-decision (AD) scheme to determine to which functional type(s) it belongs. Experimental results show that Mem-ADSVM significantly outperforms state-of-the-art predictors in terms of identifying both membrane proteins and their multi-functional types. This paper also suggests that the two-layer prediction architecture is better than the one-layer for prediction performance. For reader׳s convenience, the Mem-ADSVM server is available online at http://bioinfo.eie.polyu.edu.hk/MemADSVMServer/.
authors.affiliation: Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China. Electronic address: shibiao.wan@connect.polyu.hk.
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. Electronic address: enmwmak@polyu.edu.hk.
authors.forename: Man-Wai
authors.initials: MW
authors.lastname: Mak
authors.affiliation: Department of Electrical Engineering, Princeton University, NJ, USA. Electronic address: kung@princeton.edu.
authors.forename: Sun-Yuan
authors.initials: SY
authors.lastname: Kung
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D003657:Decision Making; D063990:Gene Ontology; D008565:Membrane Proteins / Q000032:analysis*; D015203:Reproducibility of Results; D012984:Software
chemical_list: D008565:Membrane Proteins
keywords: Adaptive-decision scheme; Gene ontology; Membrane protein type prediction; Multi-label classification; Two-layer classification