Prediction of protein-protein interactions and disease genes by machine learning

抄録

This paper presents two machine learning based methods to solve two significant problems in bioinformatics: prediction of protein-protein interactions and prediction of disease genes. Protein-protein interactions (PPI) are intrinsic to almost all cellular processes,and different computational methods recently offer chances to study PPI and related problems in molecular biology and medicine. We first use inductive logic programming (ILP) to predict PPI from integrative protein domain data and genomic/proteomic data. Starting with constructed biologically significant background knowledge of more than 220,000 ground facts, we can induce ILP significant rules that better predict protein-protein interactions in comparison with other methods. We then use semi-supervised learning methods to exploit PPI data for predicting disease genes. In addition to 3,053 disease genes known in OMIM database, we found about fifty novel putative genes that are potential in causing a number of diseases.