PPInterFinder—a mining tool for extracting causal relations on human proteins from literature by Kalpana Raja, Suresh Subramani and Jeyakumar Natarajan. (Database (2013) 2013 : bas052 doi: 10.1093/database/bas052)
Abstract:
One of the most common and challenging problem in biomedical text mining is to mine protein–protein interactions (PPIs) from MEDLINE abstracts and full-text research articles because PPIs play a major role in understanding the various biological processes and the impact of proteins in diseases. We implemented, PPInterFinder—a web-based text mining tool to extract human PPIs from biomedical literature. PPInterFinder uses relation keyword co-occurrences with protein names to extract information on PPIs from MEDLINE abstracts and consists of three phases. First, it identifies the relation keyword using a parser with Tregex and a relation keyword dictionary. Next, it automatically identifies the candidate PPI pairs with a set of rules related to PPI recognition. Finally, it extracts the relations by matching the sentence with a set of 11 specific patterns based on the syntactic nature of PPI pair. We find that PPInterFinder is capable of predicting PPIs with the accuracy of 66.05% on AIMED corpus and outperforms most of the existing systems.
Database URL: http://www.biomining-bu.in/ppinterfinder/
I thought the shortened form of the title would catch your eye. 😉
Important work for bioinformatics but it is also an example of domain specific association mining.
By focusing on a specific domain and forswearing designs on being a universal association solution, PPInterFinder produces useful results today.
A lesson that should be taken and applied to semantic mappings more generally.