Paper Info
Title
Efficient chemical-disease identification and relationship extraction using Wikipedia to improve recall.
Abstract
Awareness of the adverse effects of chemicals is important in biomedical research and healthcare. Text mining can allow timely and low-cost extraction of this knowledge from the biomedical literature. We extended our text mining solution, LeadMine, to identify diseases and chemical-induced disease relationships (CIDs). LeadMine is a dictionary/grammar-based entity recognizer and was used to recognize and normalize both chemicals and diseases to Medical Subject Headings (MeSH) IDs. The disease lexicon was obtained from three sources: MeSH, the Disease Ontology and Wikipedia. The Wikipedia dictionary was derived from pages with a disease/symptom box, or those where the page title appeared in the lexicon. Composite entities (e.g. heart and lung disease) were detected and mapped to their composite MeSH IDs. For CIDs, we developed a simple pattern-based system to find relationships within the same sentence. Our system was evaluated in the BioCreative V Chemical-Disease Relation task and achieved very good results for both disease concept ID recognition (F-1-score: 86.12%) and CIDs (F-1-score: 52.20%) on the test set. As our system was over an order of magnitude faster than other solutions evaluated on the task, we were able to apply the same system to the entirety of MEDLINE allowing us to extract a collection of over 250 000 distinct CIDs.
Year
DOI
Venue
2016
10.1093/database/baw039
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
Field
DocType
Volume
Data mining,Disease Ontology,Text mining,Information retrieval,Computer science,Grammar,Lexicon,Bioinformatics,MEDLINE,Sentence,Relationship extraction,Test set
Journal
2016
ISSN
Citations 
PageRank 
1758-0463
8
0.48
References 
Authors
4
3
Name
Order
Citations
PageRank
Daniel M Lowe1452.95
Noel M. O'Boyle258629.54
Roger A. Sayle38310.20