title: Prediction of subcellular location of apoptosis proteins combining tri-gram encoding based on PSSM and recursive feature elimination.
abstract: Knowledge of apoptosis proteins plays an important role in understanding the mechanism of programmed cell death. Obtaining information on subcellular location of apoptosis proteins is very helpful to reveal the apoptosis mechanism and understand the function of apoptosis proteins. Because of the cost in time and labor associated with large-scale wet-bench experiments, computational prediction of apoptosis proteins subcellular location becomes very important and many computational tools have been developed in the recent decades. Existing methods differ in the protein sequence representation techniques and classification algorithms adopted. In this study, we firstly introduce a sequence encoding scheme based on tri-grams computed directly from position-specific score matrices, which incorporates evolution information represented in the PSI-BLAST profile and sequence-order information. Then SVM-RFE algorithm is applied for feature selection and reduced vectors are input to a support vector machine classifier to predict subcellular location of apoptosis proteins. Jackknife tests on three widely used datasets show that our method provides the state-of-the-art performance in comparison with other existing methods.
authors.affiliation: College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.
authors.forename: Taigang
authors.initials: T
authors.lastname: Liu
authors.affiliation: College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China.
authors.forename: Peiying
authors.initials: P
authors.lastname: Tao
authors.affiliation: College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Li
authors.affiliation: College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.
authors.forename: Yufang
authors.initials: Y
authors.lastname: Qin
authors.affiliation: College of Information Technology, Shanghai Ocean University, Shanghai 201306, China. Electronic address: wchshou@163.com.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D051017:Apoptosis Regulatory Proteins / Q000378:metabolism*; D030562:Databases, Protein; D006801:Humans; D056510:Position-Specific Scoring Matrices; D021381:Protein Transport; D012372:ROC Curve; D013347:Subcellular Fractions / Q000378:metabolism; D060388:Support Vector Machine
chemical_list: D051017:Apoptosis Regulatory Proteins
keywords: Feature selection; Position-specific score matrix; Protein sequence representation; Support vector machine