Crosstalk pathway inference using topological information and biclustering of gene expression data

Detection of crosstalks among pathways is a challenging task, which requires the identification of different types of interactions associated with cellular processes. A common strategy used in bioinformatics consists in extrapolating pathway associations from the pairwise analysis of some genes related to them, using gene expression data and topological information. PET, the method proposed in this paper, goes a step further by incorporating a strategy for the detection of correlation across conditions between differentially expressed genes based on biclustering analysis. In order to evaluate the performance of this new approach, a comparison with two recently published algorithms was carried out. The methods were contrasted in the inference of pathway associations from Alzheimer disease datasets, where the new proposal presents a higher crosstalk discoveries' rate. Finally, the analysis of the biological relevance of the pathway associations inferred by PET has shown the soundness of the extracted knowledge.