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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Srinivasan, B. S., Novak, A. F., Flannick, J. A., Batzoglou, S., & McAdams, H. H. (2006). Integrated protein interaction networks for 11 microbes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3909 LNBI, pp. 1–14). Springer Verlag. https://doi.org/10.1007/11732990_1
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