DILS '09 Proceedings of the 6th International Workshop on Data Integration in the Life Sciences
Belief propagation estimation of protein and domain interactions using the sum-product algorithm
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
Advances in Artificial Intelligence - Special issue on artificial intelligence in neuroscience and systems biology: lessons learnt, open problems, and the road ahead
Large margin classifiers and Random Forests for integrated biological prediction
International Journal of Bioinformatics Research and Applications
Survey: Computational challenges in systems biology
Computer Science Review
Time-Point specific weighting improves coexpression networks from time-course experiments
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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Motivation: Several recent methods have addressed the problem of heterogeneous data integration and network prediction by modeling the noise inherent in high-throughput genomic datasets, which can dramatically improve specificity and sensitivity and allow the robust integration of datasets with heterogeneous properties. However, experimental technologies capture different biological processes with varying degrees of success, and thus, each source of genomic data can vary in relevance depending on the biological process one is interested in predicting. Accounting for this variation can significantly improve network prediction, but to our knowledge, no previous approaches have explicitly leveraged this critical information about biological context. Results: We confirm the presence of context-dependent variation in functional genomic data and propose a Bayesian approach for context-sensitive integration and query-based recovery of biological process-specific networks. By applying this method to Saccharomyces cerevisiae, we demonstrate that leveraging contextual information can significantly improve the precision of network predictions, including assignment for uncharacterized genes. We expect that this general context-sensitive approach can be applied to other organisms and prediction scenarios. Availability: A software implementation of our approach is available on request from the authors. Contact: ogt@genomics.princeton.edu Supplementary information: Supplementary data are available at http://avis.princeton.edu/contextPIXIE/