PPI finder: a mining tool for human protein-protein interactions. Export

@article{citeulike:4106225, 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/.}, author = {He, Min and Wang, Yi and Li, Wei}, citeulike-article-id = {4106225}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pone.0004554}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19234603}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19234603}, day = {23}, doi = {10.1371/journal.pone.0004554}, issn = {1932-6203}, journal = {PloS one}, keywords = {interaction, protein}, month = {February}, number = {2}, pages = {e4554+}, posted-at = {2009-02-26 13:51:38}, priority = {3}, publisher = {Public Library of Science}, title = {PPI finder: a mining tool for human protein-protein interactions.}, url = {http://dx.doi.org/10.1371/journal.pone.0004554}, volume = {4}, year = {2009} }

TY - JOUR ID - citeulike:4106225 L3 - citeulike-article-id:4106225 N2 - 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/. IS - 2 JF - PloS one SN - 1932-6203 TI - PPI finder: a mining tool for human protein-protein interactions. PB - Public Library of Science VL - 4 SP - e4554 KW - interaction KW - protein AU - He, Min AU - Wang, Yi AU - Li, Wei PY - 2009/02/23/ UR - http://dx.doi.org/10.1371/journal.pone.0004554 ER -