Design of an Integrated and Effective Platform for Gene Expression Data Mining

Abstract

Gene expression data mining is an important issue in bioinformatics research and applications since it can effectively help discover the functions of genes in high throughput way. Although there exists some software for gene expression analysis, they are insufficient in terms of efficiency, automation, flexibility and degree of integration. In this research, we design an integrated platform, namely GeneFilter, which carries the merits of high efficiency, high degree of automation, web-based anywhere flexibility and seamless integration. A standard and automated analysis flow is designed, which includes data normalization, genes filtering, genes clustering, and genes scoring so the users can easily discover the list of interesting genes, interesting expression patterns or gene clusters across different chip-array experiments. Rich kinds of visualization modules are also provided in web-based manner such that users can visualize the analysis results flexibly at anywhere. Meanwhile, useful biological knowledge like Gene Ontology and pathway information can also be retrieved easily in the same integrated platform. Hence, the GeneFilter platform serves as a highly effective platform for gene expression data mining and has been applied to different kinds of disease analysis successfully.