Knowledge discovery in bioinformatics

Abstract

Biomedical research progresses rapidly, in particular in the area of genomic and postgenomic research. Hence many challenges appear for bio-statistics and bioinformatics to deal with the large amount of data generated. After presenting some of these challenges, this chapter aims at presenting evolutionary combinatorial optimization approaches proposed to deal with knowledge discovery in bioinformatics. Therefore, the chapter will focus on three main tasks of data mining (association rules, feature selection, and clustering) widely encountered in bioinformatics applications. For each of them, a description of the task will be given as well as information about their uses in bioinformatics. Then, some evolutionary approaches proposed to cope with such a task will be exposed and discussed.