The Integration of Heterogeneous Biological Data using Bayesian Networks

• Published in: Applications and Innovations in Intelligent Systems XIV pp. 44 - 57
• Main Authors: Mcflarry, Ken, Morris, Nick, Freitas, Alex
• Format: Book Chapter
• Language: English
• Published: United Kingdom Springer London, Limited 2006; Springer London
• Subjects: Artificial intelligence; Bayesian Network; Conditional Probability Distribution; Conditional Probability Table; Markov Chain Monte Carlo; Markov Chain Monte Carlo Method
• ISBN: 9781846286650; 1846286654
• DOI: 10.1007/978-1-84628-666-7_4

Bayesian networks can provide a suitable framework for the integration of highly heterogeneous experimental data and domain knowledge from experts and ontologies. In addition, they can produce interpretable and understandable models for knowledge discovery within complex domains by providing knowledge of casual and other relationships in the data. We have developed a system using Bayesian Networks that enables domain experts to express their knowledge and integrate it with a variety of other sources such as protein-protein relationships and to cross-reference this against new knowledge discovered by the proteomics experiments. The underlying Bayesian mechanism enables a form of hypothesis testing and evaluation.