关键词: 基因芯片, 基因共表达, 双聚类, 差异, 行常量

Abstract: Bioinformaticly, it is useful to study the change process of biology, such as aging and canceration, by mining differential co-expression bicluster. The definition in the past only measured from the perspective of all set of genes, thus containing a lot of noise. Therefore, a new definition named MiSupport was put forward to measure the difference on gene level, and on the basis of MiSupport, an algorithm named MiCluster was proposed to mine the maximal differential co-expression bicluster in two real gene chips. Firstly, MiCluster constructed a differential weighted undirected sample-sample relational graph in two real-valued gene expression datasets. Secondly, the maximal differential biclusters was produced in the above differential weighted undirected sample-sample relational graph with efficiently pruning techniques and accurately generating candidates method by sample-growth and level-growth. The experimental results show that MiCluster is more efficient than the existing methods. Furthermore, the performance is evaluated by Mean Square Error (MSE) score and Gene Ontology (GO). The results show that this algorithm can find better statistical and biological significance.

Key words: gene chip, gene co-expression, bicluster, differential, constant row