Oct 8, 2018

Ontology based mining of pathogen-disease associations from literature

BioRxiv : the Preprint Server for Biology
Senay Kafkas, Robert Hoehndorf

Abstract

Background: Infectious diseases claim millions of lives especially in the developing countries each year, and resistance to drugs is an emerging threat worldwide. 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 3,420 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 ...Continue Reading

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Mentioned in this Paper

Study
Research
Pathogenic Organism
Pharmacologic Substance
Literature
Statistical Technique
Gene Ontology Project
Computational Technique
Gene Ontology
Communicable Diseases

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