Current applications of microarrays focus on precise classification or discovery of biological types, for example tumor versus normal phenotypes in cancer research. Several challenging scientific tasks in the post-genomic epoch, like hunting for the genes underlying complex diseases from genome-wide gene expression profiles and thereby building the corresponding gene networks, are largely overlooked because of the lack of an efficient analysis approach. We have thus developed an innovative ensemble decision approach, which can efficiently perform multiple gene mining tasks.An application of this approach to analyze two publicly available data sets (colon data and leukemia data) identified 20 highly significant colon cancer genes and 23 highly significant molecular signatures for refining the acute leukemia pheno-type, most of which have been verified either by biological experiments or by alternative analysis approaches. Furthermore,the globally optimal gene subsets identified by the novel approach have so far achieved the highest accuracy for classification of colon cancer tissue types. Establishment of this analysis strategy has offered the promise of advancing microarray technology as a means of deciphering the involved genetic complexities of complex diseases.

The advent of DNA microarray technology has offered the promise of casting new insights onto deciphering secrets of life by monitoring activities of thousands of genes simultaneously.Current analyses of microarray data focus on precise classification of biological types,for example,tumor versus normal tissues. A further scientific challenging task is to extract disease-relevant genes from the bewildering amounts of raw data,which is one of the most critical themes in the post-genomic era,but it is generally ignored due to lack of an efficient approach.In this paper,we present a novel ensemble method for gene extraction that can be tailored to fulfill multiple biological tasks including (i) precise classification of biological types; (ii) disease gene mining; and (iii) target-d riven gene networking. We also give a numerical application for (i) and (ii) using a public microarrary data set and set aside a separate paper to address (iii).