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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 141363, 11 pages
http://dx.doi.org/10.1155/2015/141363
Research Article

KIR Genes and Patterns Given by the A Priori Algorithm: Immunity for Haematological Malignancies

1Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí, Avenida Dr. Manuel Nava No. 8, Zona Universitaria, 78290 San Luis Potosí, ZC, Mexico
2Laboratorio de Genómica Viral y Humana, Facultad de Medicina, Universidad Autónoma de San Luis Potosí, Avenida Venustiano Carranza No. 2405, Colonia Filtros las Lomas, 78210 San Luis Potosí, CP, Mexico

Received 27 May 2015; Revised 5 August 2015; Accepted 9 August 2015

Academic Editor: Lei Chen

Copyright © 2015 J. Gilberto Rodríguez-Escobedo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Killer-cell immunoglobulin-like receptors (KIRs) are membrane proteins expressed by cells of innate and adaptive immunity. The KIR system consists of 17 genes and 614 alleles arranged into different haplotypes. KIR genes modulate susceptibility to haematological malignancies, viral infections, and autoimmune diseases. Molecular epidemiology studies rely on traditional statistical methods to identify associations between KIR genes and disease. We have previously described our results by applying support vector machines to identify associations between KIR genes and disease. However, rules specifying which haplotypes are associated with greater susceptibility to malignancies are lacking. Here we present the results of our investigation into the rules governing haematological malignancy susceptibility. We have studied the different haplotypic combinations of 17 KIR genes in 300 healthy individuals and 43 patients with haematological malignancies (25 with leukaemia and 18 with lymphomas). We compare two machine learning algorithms against traditional statistical analysis and show that the “a priori” algorithm is capable of discovering patterns unrevealed by previous algorithms and statistical approaches.