Signaling Pathway Reconstruction by Fusing Priori Knowledge

Signaling pathway construction is one of hotspots in the present bioinformatics. A novel approach where priori knowledge is fused is proposed, called Dk-NICO, where partial missing regulation relationships and regulation directions are used as data samples, and biological experiment result as priori knowledge, while HMM is used as a model for reconstructing the signaling pathway, so as to predict signaling pathway. By reconstructing MAPK pathway, it is showed that the proposed approach not only is capable of predicting gene regulation relationships, but also is capable of identifying gene regulation directions. Moreover, we apply the approach to MAPK pathway reconstruction in the case of no priori knowledge and demonstrate that, by introducing priori knowledge from direct biochemical reaction experiment, the prediction accuracy is improved.