Hybrid biogeography based simultaneous feature selection and MHC class I peptide binding prediction using support vector machines and random forests

Journal of Immunological Methods

Volumn 387, Issue 1-2, 2013, Pages 284-292

  Srivastava, Atulji ^a   Ghosh, Shameek ^b   Anantharaman, N ^c   Jayaraman, V K ^b  

^a DR D Y PATIL BIOTECHNOLOGY AND BIOINFORMATICS INSTITUTE   (India)

^b CENTRE FOR DEVELOPMENT OF ADVANCED COMPUTING   (India)

^c NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Biogeography based optimization; CoEPrA classification and regression; Feature selection; MHC class I peptide binding prediction; SVM and random forests; Weighted heuristics

Indexed keywords

MAJOR HISTOCOMPATIBILITY ANTIGEN CLASS 1;

ALGORITHM; ARTICLE; BINDING AFFINITY; BIOGEOGRAPHY BASED OPTIMIZATION; HUMAN; MACHINE LEARNING; METHODOLOGY; PRIORITY JOURNAL; PROTEIN BINDING; RANDOM FOREST; SUPPORT VECTOR MACHINE;

ALGORITHMS; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; HISTOCOMPATIBILITY ANTIGENS CLASS I; OLIGOPEPTIDES; PROTEIN BINDING; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SUPPORT VECTOR MACHINES;

EID: 84871445050     PISSN: 00221759     EISSN: 18727905     Source Type: Journal    
DOI: 10.1016/j.jim.2012.09.013     Document Type: Article

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