title: A novel algorithm combining support vector machine with the discrete wavelet transform for the prediction of protein subcellular localization.
abstract: Knowing the subcellular localization of proteins within the cell is an important step in elucidating its role in biological processes, its function and its potential as a drug target for disease diagnosis. As the number of complete genomes rapidly increases, accurate and efficient methods that automatically predict the subcellular localizations become more urgent. In the current paper, we developed a novel method that coupled the discrete wavelet transform with support vector machine based on the amino acid polarity to predict the subcellular localizations of prokaryotic and eukaryotic proteins. The results obtained by the jackknife test were quite promising, and indicated that the proposed method remarkably improved the prediction accuracy of subcellular locations, and could be as an effective and promising high-throughput method in the subcellular localization research.
authors.affiliation: Department of Chemistry, Nanchang University, Nanchang, PR China.
authors.forename: Ru-Ping
authors.initials: RP
authors.lastname: Liang
authors.forename: Shu-Yun
authors.initials: SY
authors.lastname: Huang
authors.forename: Shao-Ping
authors.initials: SP
authors.lastname: Shi
authors.forename: Xing-Yu
authors.initials: XY
authors.lastname: Sun
authors.forename: Sheng-Bao
authors.initials: SB
authors.lastname: Suo
authors.forename: Jian-Ding
authors.initials: JD
authors.lastname: Qiu
mesh_terms: D030562:Databases, Protein; D005057:Eukaryotic Cells / Q000737:chemistry / Q000378:metabolism; D042541:Intracellular Space / Q000737:chemistry* / Q000378:metabolism*; D015233:Models, Statistical; D011387:Prokaryotic Cells / Q000737:chemistry / Q000378:metabolism; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012372:ROC Curve; D060388:Support Vector Machine; D058067:Wavelet Analysis
chemical_list: D011506:Proteins