A machine learning approach for gene expression analysis and applications

High-throughput microarray technology is an important and revolutionary technique used in genomics and systems biology to analyze the expression of thousands of genes simultaneously. The popular use of this technique has resulted in enormous repositories of microarray data, for example, the Gene Expression Omnibus (GEO), maintained by the National Center for Biotechnology Information (NCBI). However, an effective approach to optimally exploit these datasets in support of specific biological studies is still lacking. Specifically, an improved method is required to integrate data from multiple sources and to select only those datasets that meet an investigator's interest. In addition, to exploit the full power of microarray data, an effective method is required to determine the relationships among genes in the selected datasets and to interpret the biological meanings behind these relationships. To address these requirements, we have developed a machine learning based approach that includes: • An effective meta-analysis method to integrate microarray data from multiple sources; the method exploits information regarding the biological context of interest provided by the biologists. • A novel and effective cluster analysis method to identify hidden patterns in selected data representing relationships between genes under the biological conditions of interest. • A novel motif finding method that discovers, not only the common transcription factor binding sites of co-regulated genes, but also the miRNA binding sites associated with the biological conditions. • A machine learning-based framework for microarray data analysis with a web application to run common analysis tasks on online.