Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition

Journal of Theoretical Biology

Volumn 242, Issue 4, 2006, Pages 941-946

  Wang, Shuang Quan ^a   Yang, Jie ^a   Chou, Kuo Chen ^a,b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b GORDON LIFE SCIENCE INSTITUTE   (United States)

Author keywords

Instance based learning; Membrane protein types; Nonlinear combination; Pseudo amino acid composition; Stacking; Support vector machine

Indexed keywords

AMINO ACID; MEMBRANE PROTEIN;

AMINO ACID; CHEMICAL COMPOSITION; DRUG; PROTEIN; RESEARCH WORK;

ACCURACY; AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; DECISION SUPPORT SYSTEM; DISCRIMINANT ANALYSIS; ENZYME ANALYSIS; MACHINE; PREDICTION; PRIORITY JOURNAL; PROCESS DEVELOPMENT; PROTEIN DETERMINATION; PROTEIN FUNCTION; PROTEIN PROTEIN INTERACTION; THEORETICAL MODEL; THEORETICAL STUDY;

ALGORITHMS; AMINO ACID SEQUENCE; ANIMALS; MEMBRANE PROTEINS; PATTERN RECOGNITION, AUTOMATED; SEQUENCE ANALYSIS, PROTEIN;

EID: 33748449804     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2006.05.006     Document Type: Article

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