Background Knowledge Enriched Data Mining for Interactome Analysis

Abstract

In recent years amount of new information generated by biological experiments keeps growing. High-throughput techniques have been developed and now are widely used to screen biological systems at genome wide level. Extracting structured knowledge from amounts of experimental information is a major challenge to bioinformatics. In this work we propose a novel approach to analyze protein interactome data. The main goal of our research is to provide a biologically meaningful explanation for the phenomena captured by high-throughput screens. We propose to reformulate several interactome analysis problems as classification problems. Consequently, we develop a transparent classification model which while perhaps sacrificing some accuracy, minimizes the amount of routine, trivial and inconsequential reasoning that must be done by a human expert. The key to designing a transparent classification model that can be easily understood by a human expert is the use of the Inductive Logic Programming approach coupled with significant involvement of background knowledge into the classification process.