Using inductive logic programming for predicting protein-protein interactions from multiple genomic data

Authors:
Tuan Nam Tran;Kenji Satou;Tu Bao Ho
Affiliations:
School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan;School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan;School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan
Venue:
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2005

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Abstract

Protein-protein interactions play an important role in many fundamental biological processes. Computational approaches for predicting protein-protein interactions are essential to infer the functions of unknown proteins, and to validate the results obtained of experimental methods on protein-protein interactions. We have developed an approach using Inductive Logic Programming (ILP) for protein-protein interaction prediction by exploiting multiple genomic data including protein-protein interaction data, SWISS-PROT database, cell cycle expression data, Gene Ontology, and InterPro database. The proposed approach demonstrates a promising result in terms of obtaining high sensitivity/specificity and comprehensible rules that are useful for predicting novel protein-protein interactions. We have also applied our method to a number of protein-protein interaction data, demonstrating an improvement on the expression profile reliability (EPR) index.