Ontology based mining of pathogen–disease associations from literature

• Published in: Journal of biomedical semantics Vol. 10; no. 1; pp. 15 - 5
• Main Authors: Kafkas, Şenay, Hoehndorf, Robert
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 18.09.2019; BioMed Central; BMC
• Subjects: Abbreviations; Anopheles; Automation; Bioinformatics; Biological Ontologies; Communicable Diseases; Computer applications; Data mining; Data Mining - methods; Developing countries; Dictionaries; Health aspects; Infection; Infectious disease; Infectious diseases; Internet; LDCs; Medical research; Mining industry; Natural language; Ontology; Pathogen; Pathogenic microorganisms; Pathogens; Pathogen–disease association; Phenotypes; Relationship extraction; Statistical methods; Taxonomy; Text mining; Tuberculosis; Saudi Arabia; Text mining; Infectious disease; Pathogen–disease association; Pathogen; Relationship extraction
• ISSN: 2041-1480; 2041-1480
• DOI: 10.1186/s13326-019-0208-2

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. 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. 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/ .