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. 2013 Jan 15:2013:bas052.
doi: 10.1093/database/bas052. Print 2013.

PPInterFinder--a mining tool for extracting causal relations on human proteins from literature

Kalpana Raja  1 , Suresh Subramani, Jeyakumar Natarajan
Affiliations

PPInterFinder--a mining tool for extracting causal relations on human proteins from literature

Kalpana Raja et al. Database (Oxford). .

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/

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Figures

Figure 1
Figure 1
Work flow of PPInterFinder.
Figure 2
Figure 2
Tregex-based algorithm for extracting the relation keyword.
Figure 3
Figure 3
Algorithm to extract PPI triplets from complex sentences with more than two proteins.
Figure 4
Figure 4
PPI extraction—methodology.
Figure 5
Figure 5
Screenshot of PPInterFinder showing input and extracted PPI pairs.
See this image and copyright information in PMC

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