ArticleProceedings of the National Academy of Sciences of the United States of America2009
Uncovering the rules for protein-protein interactions from yeast genomic data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine-learning techniques for the prediction of protein-protein interactions.Journal of biosciences · 2019Article
- Predicting the fission yeast protein interaction network.G3 (Bethesda, Md.) · 2012Article
- Article
- Mapping protein interactions between Dengue virus and its human and insect hosts.PLoS neglected tropical diseases · 2011Article
- Article
- Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation.BMC systems biology · 2010Article
- Recognition and binding of a helix-loop-helix peptide to carbonic anhydrase occurs via partly folded intermediate structures.Biophysical journal · 2010Article
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4 authors.
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Abstract
Identifying protein-protein interactions is crucial for understanding cellular functions. Genomic data provides opportunities and challenges in identifying these interactions. We uncover the rules for predicting protein-protein interactions using a frequent pattern tree (FPT) approach modified to generate a minimum set of rules (mFPT), with rule attributes constructed from the interaction features of the yeast genomic data. The mFPT prediction accuracy is benchmarked against other commonly used methods such as Bayesian networks and logistic regressions under various statistical measures. Our study indicates that mFPT outranks other methods in predicting the protein-protein interactions for the database used. We predict a new protein-protein interaction complex whose biological function is related to premRNA splicing and new protein-protein interactions within existing complexes based on the rules generated. Our method is general and can be used to discover the underlying rules for protein-protein interactions, genomic interactions, structure-function relationships, and other fields of research.
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