title: PPI finder: a mining tool for human protein-protein interactions.
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.
  
  
  RESULTS
  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/.
authors.affiliation: Key Laboratory of Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Chaoyang District, Beijing, China.
authors.forename: Min
authors.initials: M
authors.lastname: He
authors.forename: Yi
authors.initials: Y
authors.lastname: Wang
authors.forename: Wei
authors.initials: W
authors.lastname: Li
mesh_terms: D019295:Computational Biology / Q000379:methods*; D006801:Humans; D020407:Internet; D025941:Protein Interaction Mapping / Q000379:methods*; D039781:PubMed; D012984:Software