Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Graph algorithms for biological systems analysis
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Multiple Alignment of Biological Networks: A Flexible Approach
CPM '09 Proceedings of the 20th Annual Symposium on Combinatorial Pattern Matching
Pairwise global alignment of protein interaction networks by matching neighborhood topology
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Automatic parameter learning for multiple network alignment
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Semi-supervised protein function prediction via sequential linear neighborhood propagation
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
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We have combined four different types of functional genomic data to create high coverage protein interaction networks for 11 microbes. Our integration algorithm naturally handles statistically dependent predictors and automatically corrects for differing noise levels and data corruption in different evidence sources. We find that many of the predictions in each integrated network hinge on moderate but consistent evidence from multiple sources rather than strong evidence from a single source, yielding novel biology which would be missed if a single data source such as coexpression or coinheritance was used in isolation. In addition to statistical analysis, we demonstrate via case study that these subtle interactions can discover new aspects of even well studied functional modules. Our work represents the largest collection of probabilistic protein interaction networks compiled to date, and our methods can be applied to any sequenced organism and any kind of experimental or computational technique which produces pairwise measures of protein interaction.