Biclustering is an unsupervised classification technique that plays an increasingly important role in the study of modern biology. This data mining technique has provided answers to several challenges raised by the analysis of biological data and more particularly the analysis of gene expression data. It aims to cluster simultaneously genes and conditions. These unsupervised techniques are based essentially on the assumption that the extraction of the co-expressed genes allows to have co-regulated genes. In addition, the integration of biological information in the search process may induce to the extraction of relevant and non-trivial biclusters. Therefore, this work proposes an evolutionary algorithm based on local search method that relies on biological knowledge. An experimental study is achieved on real microarray datasets to evaluate the performance of the proposed algorithm. The assessment and the comparison are based on statistical and biological criteria. A cross-validation experiment is also used to estimate its accuracy. Promising results are obtained. They demonstrate the importance of the integration of the biological knowledge in the biclustering process to foster the efficiency and to promote the discovery of non-trivial and biologically relevant biclusters.

比对是一种无监督的分类技术，在现代生物学的研究中起着越来越重要的作用。这种数据挖掘技术已经为生物学数据分析，尤其是基因表达数据分析提出的若干挑战提供了答案。它旨在同时聚类基因和条件。这些无监督的技术基本上是基于这样的假设，即共表达基因的提取允许具有共同调控的基因。此外，在搜索过程中整合生物信息可能会导致提取相关的和非平凡的二类聚类。因此，这项工作提出了一种基于局部搜索方法的进化算法，该算法依赖于生物学知识。对真实的微阵列数据集进行了实验研究，以评估所提出算法的性能。评估和比较基于统计和生物学标准。交叉验证实验也用于估计其准确性。获得了有希望的结果。他们证明了在双集群过程中整合生物学知识的重要性，以提高效率并促进发现非平凡和生物学相关的双集群。