%0 Journal Article %1 PPIFinder.2009 %A He, Min %A Wang, Yi %A Li, Wei %D 2009 %J PLoS one %K CAT CAT-ONLINE-SW CAT-REL-COOR CAT-REL-DBS CAT-REL-VERB PPIFinder extraction interaction relation words %N 2 %P e4554 %T PPI Finder: A Mining Tool for Human Protein-Protein Interactions %U http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2641004/pdf/pone.0004554.pdf %V 4 %X Background: The exponential increase of published biomedical literature prompts the use of text mining tools to manage the information overload automatically. One of the most common applications is to mine protein-protein interactions (PPIs) from PubMed abstracts. Currently, most tools in mining PPIs from literature are using co-occurrence-based approaches or rule-based approaches. Hybrid methods (frame-based approaches) by combining these two methods may have better performance in predicting PPIs. However, the predicted PPIs from these methods are rarely evaluated by known PPI databases and co-occurred terms in Gene Ontology (GO) database. Methodology/Principal Findings: We here developed a web-based tool, PPI Finder, to mine human PPIs from PubMed abstracts based on their co-occurrences and interaction words, followed by evidences in human PPI databases and shared terms in GO database. Only 28% of the co-occurred pairs in PubMed abstracts appeared in any of the commonly used human PPI databases (HPRD, BioGRID and BIND). On the other hand, of the known PPIs in HPRD, 69% showed co- occurrences in the literature, and 65% shared GO terms. Conclusions: PPI Finder provides a useful tool for biologists to uncover potential novel PPIs. It is freely accessible at http:// liweilab.genetics.ac.cn/tm/.

@article{PPIFinder.2009, abstract = {Background: The exponential increase of published biomedical literature prompts the use of text mining tools to manage the information overload automatically. One of the most common applications is to mine protein-protein interactions (PPIs) from PubMed abstracts. Currently, most tools in mining PPIs from literature are using co-occurrence-based approaches or rule-based approaches. Hybrid methods (frame-based approaches) by combining these two methods may have better performance in predicting PPIs. However, the predicted PPIs from these methods are rarely evaluated by known PPI databases and co-occurred terms in Gene Ontology (GO) database. Methodology/Principal Findings: We here developed a web-based tool, PPI Finder, to mine human PPIs from PubMed abstracts based on their co-occurrences and interaction words, followed by evidences in human PPI databases and shared terms in GO database. Only 28% of the co-occurred pairs in PubMed abstracts appeared in any of the commonly used human PPI databases (HPRD, BioGRID and BIND). On the other hand, of the known PPIs in HPRD, 69% showed co- occurrences in the literature, and 65% shared GO terms. Conclusions: PPI Finder provides a useful tool for biologists to uncover potential novel PPIs. It is freely accessible at http:// liweilab.genetics.ac.cn/tm/.}, added-at = {2010-09-20T08:41:15.000+0200}, author = {He, Min and Wang, Yi and Li, Wei}, biburl = {https://www.bibsonomy.org/bibtex/2dc1ec03144c492ff61f8df1ef9d6f25d/huiyangsfsu}, interhash = {62376984b8d169e4665f52406599bf36}, intrahash = {dc1ec03144c492ff61f8df1ef9d6f25d}, journal = {PLoS one}, keywords = {CAT CAT-ONLINE-SW CAT-REL-COOR CAT-REL-DBS CAT-REL-VERB PPIFinder extraction interaction relation words}, month = feb, number = 2, pages = {e4554}, timestamp = {2010-11-12T02:20:23.000+0100}, title = {PPI Finder: A Mining Tool for Human Protein-Protein Interactions}, url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2641004/pdf/pone.0004554.pdf}, volume = 4, year = 2009 }