Ontology based mining of pathogen–disease associations from literature

BACKGROUND: Infectious diseases claim millions of lives especially in the developing countries each year. Identification of causative pathogens accurately and rapidly plays a key role in the success of treatment. To support infectious disease research and mechanisms of infection, there is a need for an open resource on pathogen–disease associations that can be utilized in computational studies. A large number of pathogen–disease associations is available from the literature in unstructured form and we need automated methods to extract the data. RESULTS: We developed a text mining system designed for extracting pathogen–disease relations from literature. Our approach utilizes background knowledge from an ontology and statistical methods for extracting associations between pathogens and diseases. In total, we extracted a total of 3420 pathogen–disease associations from literature. We integrated our literature-derived associations into a database which links pathogens to their phenotypes for supporting infectious disease research. CONCLUSIONS: To the best of our knowledge, we present the first study focusing on extracting pathogen–disease associations from publications. We believe the text mined data can be utilized as a valuable resource for infectious disease research. All the data is publicly available from https://github.com/bio-ontology-research-group/padimi and through a public SPARQL endpoint from http://patho.phenomebrowser.net/.