Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An integrated approach to predictive genomic analytics
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Learning biological networks via bootstrapping with optimized go-based gene similarity
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
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Many algorithms have been proposed for deriving regulatory networks from microarray gene expression data. The performance of such algorithms is often measured by how well the resulting network can recreate the gene expression data that it was derived from. However, this kind of performance does not necessarily mean that the regulatory hypotheses in the network are biologically plausible. We therefore propose a method for assessing the biological plausibility of regulatory hypotheses using prior knowledge in the form of regulatory pathway databases and Gene Ontology-based annotation of gene products. A set of templates is derived by generalising from known interactions to typical properties of interacting gene product pairs. By searching for matches in this set of templates, the plausibility of regulatory hypotheses can be assessed. We evaluate to what degree the collection of templates can separate true from false positive interactions, and we illustrate the practical use of the method by applying it to an example network reconstruction problem.