Integrating Gene Expression Data and Pathway Knowledge for In Silico Hypothesis Generation with IMPRes

Although biologically meaningful modules can often be detected by many existing informatics tools, it is still hard to interpret or make use of the results towards in silico hypothesis generation and testing. To address this gap, we have developed the IMPRes (Integrative MultiOmics Pathway Resolution) algorithm, a new step-wise active pathway detection method using a dynamic programming approach. This approach enables the network detection one step at a time, making it easy for researchers to trace the pathways, and leading to more accurate drug design and more effective treatment strategies. The evaluation experiments conducted on two 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 have provided several insights on involved genes and mechanisms in lung cancer, which had not been discovered earlier. IMPRes visualization tool is available as a web service at http://digbio.missouri.edu/impres.