Combining Bayesian Networks and Decision Trees to Predict Drosophila melanogaster Protein-Protein Interactions

Protein-protein interactions are important in many aspects of cellular processes. Discovery of protein interactions that take place within a cell can provide a starting point for understanding biological regulatory pathways. High-throughput experimental screens developed so far show high error rates in terms of false positives and false negatives. There is thus a great need for new computational approaches to enable the prediction of new protein-protein interactions and to enhance the reliability of experimentally derived interaction maps. Many of the computational approaches developed thus far are based on strong biological assumptions, resulting in biases towards certain types of predictions. As a first step towards a more complete and accurate interaction map, we propose to predict protein-protein interactions using existing experimental data combined with the Gene Ontology (GO) annotations of proteins. We do not use strong prior rules about GO patterns and proteinprotein interactions and thus avoid biases associated with various assumptions. We show that GO annotations can be a useful predictor for proteinprotein interactions and that prediction performance can be improved by combining the results from both decision trees and Bayesian networks.