JSAI Technical Report, Type 2 SIG
Online ISSN : 2436-5556
Prediction of protein-protein interactions and disease genes by machine learning
Thanh PHUONGNGUYENTu BAOHO
Author information
RESEARCH REPORT / TECHNICAL REPORT FREE ACCESS

2007 Volume 2007 Issue DMSM-A702 Pages 04-

Details
Abstract

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.

Content from these authors
© 2007 Authors
Previous article Next article
feedback
Top