Evolutionary Data Mining for Genomics

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Author(s): Laetitia Jourdan (University of Lille, France)
Copyright: 2009
Pages: 6
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch127

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Abstract

Knowledge discovery from genomic data has become an important research area for biologists. Nowadays, a lot of data is available on the web, but the corresponding knowledge is not necessarly also available. For example, the first draft of the human genome, which contains 3×109 letters, has been achieved in June 2000, but up to now only a small part of the hidden knowledge has been discovered. The aim of bioinformatics is to bring together biology, computer science, mathematics, statistics and information theory to analyze biological data for interpretation and prediction. Hence many problems encountered while studying genomic data may be modeled as data mining tasks, such as feature selection, classification, clustering, and association rule discovery. An important characteristic of genomic applications is the large amount of data to analyze and it is, most of the time, not possible to enumerate all the possibilities. Therefore, we propose to model these knowledge discovery tasks as combinatorial optimization tasks, in order to apply efficient optimization algorithms to extract knowledge from large datasets. To design an efficient optimization algorithm, several aspects have to be considered. The main one is the choice of the type of resolution method according to the characteristics of the problem. Is it an easy problem, for which a polynomial algorithm may be found? If yes, let us design such an algorithm. Unfortunately, most of the time the response to the question is ‘NO’ and only heuristics, that may find good but not necessarily optimal solutions, can be used. In our approach we focus on evolutionary computation, which has already shown an interesting ability to solve highly complex combinatorial problems. In this chapter, we will show the efficiency of such an approach while describing the main steps required to solve data mining problems from genomics with evolutionary algorithms. We will illustrate these steps with a real problem.

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