Uncovering the rules for protein-protein interactions from yeast genomic data. Export

@article{citeulike:4091079, 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.}, author = {Wang, Jin and Li, Chunhe and Wang, Erkang and Wang, Xidi}, citeulike-article-id = {4091079}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0806427106}, citeulike-linkout-1 = {http://www.pnas.org/content/0806427106v1//.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/0806427106v1//.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19237561}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19237561}, day = {10}, doi = {10.1073/pnas.0806427106}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {biology, complex}, month = {March}, number = {10}, pages = {3752--3757}, posted-at = {2009-06-23 07:58:24}, priority = {2}, title = {Uncovering the rules for protein-protein interactions from yeast genomic data.}, url = {http://dx.doi.org/10.1073/pnas.0806427106}, volume = {106}, year = {2009} }

TY - JOUR ID - citeulike:4091079 L3 - citeulike-article-id:4091079 N2 - 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. IS - 10 JF - Proceedings of the National Academy of Sciences of the United States of America SN - 1091-6490 EP - 3757 TI - Uncovering the rules for protein-protein interactions from yeast genomic data. VL - 106 SP - 3752 KW - biology KW - complex AU - Wang, Jin AU - Li, Chunhe AU - Wang, Erkang AU - Wang, Xidi PY - 2009/03/10/ UR - http://dx.doi.org/10.1073/pnas.0806427106 ER -