Integrating protein-protein interactions and text mining for protein function prediction Export

@article{citeulike:3048357, abstract = {BACKGROUND:Functional annotation of proteins remains a challenging task. Currently the scientific literature serves as the main source for yet uncurated functional annotations, but curation work is slow and expensive. Automatic techniques that support this work are still lacking reliability. We developed a method to identify conserved protein interaction graphs and to predict missing protein functions from orthologs in these graphs. To enhance the precision of the results, we furthermore implemented a procedure that validates all predictions based on findings reported in the literature.RESULTS:Using this procedure, more than 80\% of the GO annotations for proteins with highly conserved orthologs that are available in UniProtKb/Swiss-Prot could be verified automatically. For a subset of proteins we predicted new GO annotations that were not available in UniProtKb/Swiss-Prot. All predictions were correct (100\% precision) according to the verifications from a trained curator.CONCLUSION:Our method of integrating CCSs and literature mining is thus a highly reliable approach to predict GO annotations for weakly characterized proteins with orthologs.}, author = {Jaeger, Samira and Gaudan, Sylvain and Leser, Ulf and Schuhmann, Dietrich R.}, citeulike-article-id = {3048357}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-9-S8-S2}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18673526}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18673526}, doi = {10.1186/1471-2105-9-S8-S2}, journal = {BMC Bioinformatics}, keywords = {functional, prediction, protein}, number = {Suppl 8}, posted-at = {2008-08-13 08:30:57}, priority = {2}, title = {Integrating protein-protein interactions and text mining for protein function prediction}, url = {http://dx.doi.org/10.1186/1471-2105-9-S8-S2}, volume = {9}, year = {2008} }

TY - JOUR ID - citeulike:3048357 L3 - citeulike-article-id:3048357 N2 - BACKGROUND:Functional annotation of proteins remains a challenging task. Currently the scientific literature serves as the main source for yet uncurated functional annotations, but curation work is slow and expensive. Automatic techniques that support this work are still lacking reliability. We developed a method to identify conserved protein interaction graphs and to predict missing protein functions from orthologs in these graphs. To enhance the precision of the results, we furthermore implemented a procedure that validates all predictions based on findings reported in the literature.RESULTS:Using this procedure, more than 80% of the GO annotations for proteins with highly conserved orthologs that are available in UniProtKb/Swiss-Prot could be verified automatically. For a subset of proteins we predicted new GO annotations that were not available in UniProtKb/Swiss-Prot. All predictions were correct (100% precision) according to the verifications from a trained curator.CONCLUSION:Our method of integrating CCSs and literature mining is thus a highly reliable approach to predict GO annotations for weakly characterized proteins with orthologs. IS - Suppl 8 JF - BMC Bioinformatics TI - Integrating protein-protein interactions and text mining for protein function prediction VL - 9 KW - functional KW - prediction KW - protein AU - Jaeger, Samira AU - Gaudan, Sylvain AU - Leser, Ulf AU - Schuhmann, Dietrich PY - 2008/// UR - http://dx.doi.org/10.1186/1471-2105-9-S8-S2 ER -