Fouille de données biologiques: vers une Représentation Booléenne des Règles d'Association

The advent of new biotechnologies has led, in recent years, accumulating data on the genomes of pathogens epidemiology. As against the exploitation of genomic data do not follow the pace of discovery, then the search of biological data, particularly epidemiological nature has imposed itself to help find some answers to questions arises that the epidemiologist on specific diseases. Hence, the problem addressed by this study is that data mining of biological Mycobacterium Tuberculosis responsible for tuberculosis. We propose a process of data-enough to generate new knowledge that will be profitable and grown at two levels: Take advantage of the specialist field, through the extraction of particular patterns in the rules of association which help to better understand the pathology. Thereafter, the extracted association rules are modeled by the Boolean principle adopted by the cellular machinery CASI (Cellular Automaton for Symbolic Induction). The purpose of this modeling by the Boolean principle to reduce the complexity of storage and response time.