Prediction of candidate genes for neuropsychiatric disorders using feature-based enrichment
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Prioritization of novel disease genes is a major challenge in bioinformatics. The large amount of data collected from modern biological experiments makes it difficult for biologists to determine how information on a particular gene relates to a disease or phenotype, whereas performing exhaustive experiments on all possible combinations is impossible. Computational approaches are thus crucial in automating the process of extracting critical annotation and patterns and predicting relevant novel genes with high confidence. In this paper we propose a new method for prioritizing disease genes using both annotations on the genes as well as the underlying gene interaction network. Our approach is unique in that it uses a conditional random field to simultaneously exploit both network and annotation information directly without attempting to convert the network information into features or vice versa. Performance evaluation on standard data sets achieves a median ranking of 29% and over 0.6 area under curve value in cross-validation experiments on 42 diseases.