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Literature mining of host–pathogen interactions: comparing feature-based supervised learning and language-based approaches

Thieu, Thanh, Joshi, Sneha, Warren, Samantha, Korkin, Dmitry
Bioinformatics 2012 v.28 no.6 pp. 867-875
artificial intelligence, bioinformatics, computer analysis, databases, genes, host-pathogen relationships, infectious diseases, information retrieval, journals, models, pathogens, protein-protein interactions, proteins
MOTIVATION: In an infectious disease, the pathogen's strategy to enter the host organism and breach its immune defenses often involves interactions between the host and pathogen proteins. Currently, the experimental data on host–pathogen interactions (HPIs) are scattered across multiple databases, which are often specialized to target a specific disease or host organism. An accurate and efficient method for the automated extraction of HPIs from biomedical literature is crucial for creating a unified repository of HPI data. RESULTS: Here, we introduce and compare two new approaches to automatically detect whether the title or abstract of a PubMed publication contains HPI data, and extract the information about organisms and proteins involved in the interaction. The first approach is a feature-based supervised learning method using support vector machines (SVMs). The SVM models are trained on the features derived from the individual sentences. These features include names of the host/pathogen organisms and corresponding proteins or genes, keywords describing HPI-specific information, more general protein–protein interaction information, experimental methods and other statistical information. The language-based method employed a link grammar parser combined with semantic patterns derived from the training examples. The approaches have been trained and tested on manually curated HPI data. When compared to a naïve approach based on the existing protein–protein interaction literature mining method, our approaches demonstrated higher accuracy and recall in the classification task. The most accurate, feature-based, approach achieved 66–73% accuracy, depending on the test protocol. AVAILABILITY: Both approaches are available through PHILM web-server: CONTACT: Supplementary information: Supplementary data are available at Bioinformatics online.