Support vector machine algorithms in the search of KIR gene associations with disease

Killer-cell immunoglobulin-like receptors (KIR) are membrane proteins expressed by natural killer cells and CD8 lymphocytes. The KIR system consists of 17 genes and 614 alleles, some of which bind human leukocyte antigens (HLA). Both KIR and HLA modulate susceptibility to haematological malignancies, viral infections and autoimmune diseases. Molecular epidemiology studies employ traditional statistical methods to identify links between KIR genes and disease. Here we describe our results at applying artificial intelligence algorithms (support vector machines) to identify associations between KIR genes and disease. We demonstrate that these algorithms are capable of classifying samples into healthy and diseased groups based solely on KIR genotype with potential use in clinical decision support systems. © 2013 Elsevier Ltd.

Artificial intelligence; Computational biology; Immunogenetics; KIR; NK cells Artificial intelligence algorithms; Clinical decision support systems; Computational biology; Human leukocyte antigen; Immunogenetics; KIR; NK cells; Support vector machine algorithm; Algorithms; Artificial intelligence; Bioinformatics; Biological membranes; Decision support systems; Genes; Support vector machines; killer cell immunoglobulin like receptor; adult; article; artificial intelligence; clinical decision making; controlled study; decision support system; disease association; female; gene identification; genetic algorithm; genetic association; genotype; human; major clinical study; male; priority journal; support vector machine; Artificial intelligence; Computational biology; Immunogenetics; KIR; NK cells; Algorithms; Genotype; HLA Antigens; Humans; Molecular Epidemiology; Receptors, KIR; Sequence Analysis; Support Vector Machines

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