Discovering signal transduction networks using signaling domain-domain interactions.

The objective of this paper is twofold. One objective is to present a method of predicting signaling domain-domain interactions (signaling DDI) using inductive logic programming (ILP), and the other is to present a method of discovering signal transduction networks (STN) using signaling DDI. The research on computational methods for discovering signal transduction networks (STN) has received much attention because of the importance of STN to transmit inter- and intra-cellular signals. Unlike previous STN works functioning at the protein/gene levels, our STN method functions at the protein domain level, on signal domain interactions, which allows discovering more reliable and stable STN. We can mostly reconstruct the STN of yeast MAPK pathways from the inferred signaling domain interactions, with coverage of 85%. For the problem of prediction of signaling DDI, we have successfully constructed a database of more than twenty four thousand ground facts from five popular genomic and proteomic databases. We also showed the advantage of ILP in signaling DDI prediction from the constructed database, with high sensitivity (88%) and accuracy (83%). Studying yeast MAPK STN, we found some new signaling domain interactions that do not exist in the well-known InterDom database. Supplementary materials are now available from http://www.jaist.ac.jp/s0560205/STP_DDI/.