title: Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition.
abstract: The successful prediction of protein subcellular localization directly from protein primary sequence is useful to protein function prediction and drug discovery. In this paper, by using the concept of pseudo amino acid composition (PseAAC), the mycobacterial proteins are studied and predicted by support vector machine (SVM) and increment of diversity combined with modified Mahalanobis Discriminant (IDQD). The results of jackknife cross-validation for 450 non-redundant proteins show that the overall predicted successful rates of SVM and IDQD are 82.2% and 79.1%, respectively. Compared with other existing methods, SVM combined with PseAAC display higher accuracies.
authors.affiliation: Center for Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China. hlbedh@yahoo.cn.
authors.forename: Hao
authors.initials: H
authors.lastname: Lin
authors.forename: Hui
authors.initials: H
authors.lastname: Ding
authors.forename: Feng-Biao
authors.initials: FB
authors.lastname: Guo
authors.forename: An-Ying
authors.initials: AY
authors.lastname: Zhang
authors.forename: Jian
authors.initials: J
authors.lastname: Huang
mesh_terms: D000596:Amino Acids / Q000032:analysis; D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000737:chemistry* / Q000378:metabolism*; D030562:Databases, Protein; D016002:Discriminant Analysis; D008954:Models, Biological; D009161:Mycobacterium / Q000378:metabolism*; D013347:Subcellular Fractions / Q000378:metabolism
chemical_list: D000596:Amino Acids; D001426:Bacterial Proteins