title: Prediction of nuclear receptors with optimal pseudo amino acid composition.
abstract: Nuclear receptors are involved in multiple cellular signaling pathways that affect and regulate processes such as organ development and maintenance, ion transport, homeostasis, and apoptosis. In this article, an optimal pseudo amino acid composition based on physicochemical characters of amino acids is suggested to represent proteins for predicting the subfamilies of nuclear receptors. Six physicochemical characters of amino acids were adopted to generate the protein sequence features via web server PseAAC. The optimal values of the rank of correlation factor and the weighting factor about PseAAC were determined to get the appropriate descriptor of proteins that leads to the best performance. A nonredundant dataset of nuclear receptors in four subfamilies is constructed to evaluate the method using support vector machines. An overall accuracy of 99.6% was achieved in the fivefold cross-validation test as well as the jackknife test, and an overall accuracy of 98.4% was reached in a blind dataset test. The performance is very competitive with that of some previous methods.
authors.affiliation: Department of Health Statistics, Second Military Medical University, Shanghai 200433, China.
authors.forename: Qing-Bin
authors.initials: QB
authors.lastname: Gao
authors.forename: Zhi-Chao
authors.initials: ZC
authors.lastname: Jin
authors.forename: Xiao-Fei
authors.initials: XF
authors.lastname: Ye
authors.forename: Cheng
authors.initials: C
authors.lastname: Wu
authors.forename: Jia
authors.initials: J
authors.lastname: He
mesh_terms: D000596:Amino Acids / Q000737:chemistry*; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D018160:Receptors, Cytoplasmic and Nuclear / Q000737:chemistry / Q000145:classification*; D020539:Sequence Analysis, Protein / Q000379:methods
chemical_list: D000596:Amino Acids; D018160:Receptors, Cytoplasmic and Nuclear