Thanh Phuong Nguyen (phuong@jaist.ac.jp)
Tu Bao Ho (bao@jaist.ac.jp)
Japan Advanced Institute of Science and Technology,
1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
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/.