Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM

Journal of Biomolecular Structure and Dynamics

Volumn 29, Issue 6, 2012, Pages 1138-1146

  Zhang, Shengli ^a   Ye, Feng ^a   Yuan, Xiguo ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Principal component analysis; Protein structural class; PSSM; Support vector machine; Transition probability matrix

Indexed keywords

ACCURACY; ANALYTIC METHOD; ARTICLE; COST EFFECTIVENESS ANALYSIS; INTERMETHOD COMPARISON; JACKKNIFE TEST; POSITION SPECIFIC SCORE MATRIX; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN DATABASE; PROTEIN STRUCTURE; SEQUENCE HOMOLOGY; SUPPORT VECTOR MACHINE; AMINO ACID SEQUENCE; CHEMISTRY; CLASSIFICATION; METABOLISM; METHODOLOGY; PROTEIN CONFORMATION; SEQUENCE ANALYSIS;

PROTEIN;

AMINO ACID SEQUENCE; DATABASES, PROTEIN; PRINCIPAL COMPONENT ANALYSIS; PROTEIN CONFORMATION; PROTEINS; SEQUENCE ANALYSIS, PROTEIN; SUPPORT VECTOR MACHINES;

EID: 84865383117     PISSN: 07391102     EISSN: None     Source Type: Journal    
DOI: 10.1080/07391102.2011.672627     Document Type: Article

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