Bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline. The ultimate goal of the field is to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be established. The rationale for applying computational approaches to facilitate the understanding of various biological processes includes a more global perspective in experimental design and the ability to capitalize on the emerging technology of data mining. One of the main aims of current genetics research is to discover functional relationship between genotype and phenotype. Identifying the causal genetic variants and their functional patterns may greatly facilitate the preventive and diagnosis and biochemical understanding of genetic diseases. This so-called gene mapping. During recent years, there has been growing interest in using data mining methods in gene mapping, motivated by the lack of success of the traditional approaches for complex diseases, and also by the intriguing possibility of simultaneous detection of multiple loci. The data mining methods for linkage disequilibrium mapping can be categorized into three groups including dir=ltr The association gene mapping methods based on the haplotype clustering analysis are vastly used to localize a mutation in a gene sequence. In many cases the locations that are found based on these methods have large errors. In this work, we present a robust technique to lower the mean error of the association gene mapping in the haplotype clustering analysis. In this technique, we utilize the information gain to select a set of important features (i.e., markers) that are used in the clustering process. In other words, each marker is assigned a rank and then the high ranked markers are fed into the HapMiner algorithm for localizing the disease. In order to justify the proposed approach, We have applied the performance of our technique on a set of simulated dataset. The experiments show a significant reduction in the mean error of the gene mapping.
