A dynamic programing approach to integrate gene expression data and network information for pathway model generation

MOTIVATION As large amounts of biological data continue to be rapidly generated, a major focus of bioinformatics research has been aimed towards integrating these data to identify active pathways or modules under certain experimental conditions or phenotypes. Although biologically significant modules can often be detected globally by many existing methods, it is often hard to interpret or make use of the results towards pathway model generation and testing. RESULTS To address this gap, we have developed the IMPRes algorithm, a new step-wise active pathway detection method using a dynamic programming approach. IMPRes takes advantage of the existing pathway interaction knowledge in KEGG. Omics data are then used to assign penalties to genes, interactions, and pathways. Finally, starting from one or multiple seed genes, a shortest path algorithm is applied to detect downstream pathways that best explain the gene expression data. Since dynamic programming enables the detection one step at a time, it is easy for researchers to trace the pathways, which may lead to more accurate drug design and more effective treatment strategies. The evaluation experiments conducted on three yeast data sets have shown that IMPRes can achieve competitive or better performance than other state-of-the-art methods. Furthermore, a case study on human lung cancer data set was performed and we provided several insights on genes and mechanisms involved in lung cancer, which had not been discovered before. AVAILABILITY IMPRes visualization tool is available via web server at http://digbio.missouri.edu/impres.