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

Accurate detection of peptides binding to specific Major Histocompatibility Complex Class I (MHC-I) molecules is extremely important for understanding the underlying process of the immune system, as well as for effective vaccine design and developing immunotherapies. Development of learning algorith...

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Veröffentlicht in:	Journal of immunological methods 2013-01, Vol.387 (1-2), p.284-292
Hauptverfasser:	Srivastava, Atulji, Ghosh, Shameek, Anantharaman, N., Jayaraman, V.K.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Biogeography Biogeography based optimization Cluster Analysis CoEPrA classification and regression Competition Computational Biology - methods cost effectiveness data collection Evolution Feature selection Forests Histocompatibility Antigens Class I - immunology Histocompatibility Antigens Class I - metabolism Hybrids Immune system Immunotherapy Internet Learning algorithms Major histocompatibility complex MHC class I peptide binding prediction Oligopeptides - classification Oligopeptides - immunology Oligopeptides - metabolism peptides prediction Problem solving Protein Binding - immunology Regression Analysis Reproducibility of Results Support Vector Machine support vector machines SVM and random forests vaccine development Vaccines Weighted heuristics
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