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


CONCLUSIONS
Our method of integrating CCSs and literature mining is thus a highly reliable approach to predict GO annotations for weakly characterized proteins with orthologs.
Jaeger
Samira
S
Knowledge Management in Bioinformatics, Humboldt-University Berlin, Unter den Linden 6, 10099 Berlin, Germany. sjaeger@informatik.hu-berlin.de
Gaudan
Sylvain
S
Leser
Ulf
U
Rebholz-Schuhmann
Dietrich
D
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D030562:Databases, Protein; D011506:Proteins / Q000737:chemistry* / Q000502:physiology*; D015203:Reproducibility of Results; D009626:Terminology as Topic
D011506:Proteins