A Bayesian integration model for improved gene functional inference from heterogeneous data sources

Authors:
Noor Alaydie;Chandan K. Reddy;Farshad Fotouhi
Affiliations:
Wayne State University, Detroit, MI;Wayne State University, Detroit, MI;Wayne State University, Detroit, MI
Venue:
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Year:
2011

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Abstract

Increasing amounts of biological data from various sources are being made available by high-throughput genomic technologies. However, no single biological data source analysis can fully unravel the complexities of the hierarchical gene function prediction. Therefore, the integration of multiple data sources is required to acquire a more precise understanding of the role of genes in the living organisms. In this paper, we develop a Hierarchical Bayesian iNtegration algorithm, HiBiN, a general framework that uses Bayesian reasoning to integrate heterogeneous data sources for accurate gene function prediction. The system uses posterior probabilities to assign class memberships to samples using multiple data sources while maintaining the hierarchical constraint that governs the annotation of genes. We demonstrate that the integration of the diverse datasets significantly improves the classification quality for hierarchical gene function prediction in terms of several measures, compared to single-source prediction models and fused-flat model, which are the baselines we compared against.