Nguyen Thanh Phuong, Ho Tu Bao
Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan.
Genome Inform. 2006;17(2):35-45.
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/.
本文的目标有两个。一个目标是提出一种使用归纳逻辑编程（ILP）预测信号域-域相互作用（信号DDI）的方法，另一个目标是提出一种使用信号DDI发现信号转导网络（STN）的方法。由于STN在传递细胞间和细胞内信号方面的重要性，关于发现信号转导网络（STN）的计算方法的研究受到了广泛关注。与之前在蛋白质/基因水平起作用的STN研究不同，我们的STN方法在蛋白质结构域水平上，基于信号域相互作用起作用，这使得能够发现更可靠和稳定的STN。我们能够从推断出的信号域相互作用中大致重建酵母MAPK途径的STN，覆盖率为85%。对于信号DDI的预测问题，我们已经从五个流行的基因组和蛋白质组数据库成功构建了一个包含超过两万四千个基本事实的数据库。我们还从构建的数据库中展示了ILP在信号DDI预测中的优势，具有高灵敏度（88%）和准确性（83%）。通过研究酵母MAPK STN，我们发现了一些在著名的InterDom数据库中不存在的新的信号域相互作用。补充材料可从http://www.jaist.ac.jp/s0560205/STP_DDI/获取。
