ON DATA MINING IN BIOINFORMATICS

Abstract

Using data mining techniques in bioinformatics is an attractive re­ search area. This thesis is devoted to extract the association rules (as one of the basic data mining tasks) included in DNA microarray datasets. The thesis presents a study of various algorithms concerning the row and column enumeration based methods, and it concentrates on analysing the MAXCONF algorithm. Biological evaluations show that the MAXCONF algorithm provides excellent results compared with other algorithms applied to DNA microarray. It mines maxi­ mal high confident association rules, MHCR, from DNA microarray dataset. However, it has two drawbacks which are represented in consuming expensive computations and producing many useless rules. Therefore, a slight modification of the algorithm is proposed for fixing these drawbacks. Moreover, two versions of the MAXCONF algorithm have been implemented: the first (MAXCONFJ) mines MHCR from up-expressed genes dataset. The second (MAXCONF2) mines MHCR from up/down-expressed genes dataset. Two new sequential algorithms (MMHCR and MCR-Miner) are proposed for mining maximal high confident association rules from up-expressed and up/down-expressed genes dataset respectively. Re­ ally, these algorithms are the column enumeration based analog of MAXCONFl and MAXCONF2 algorithms. In order to build these