An end-to-end system for extracting chemical-induced disease relations in biomedical literature. CD-REST consists of two main components: (1) a chemical and disease named entity recognition and normalization module, which employs the Conditional Random Fields algorithm for entity recognition and a Vector Space Model-based approach for normalization; and (2) a relation extraction module that classifies both sentence-level and document-level candidate drug-disease pairs by support vector machines. Our system achieved the best performance on the chemical-induced disease relation extraction subtask in the BioCreative V CDR Track, demonstrating the effectiveness of our proposed machine learning-based approaches for automatic extraction of chemical-induced disease relations in biomedical literature. The CD-REST system provides web services using HTTP POST request.
School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
CD-REST funding source(s)
This project was supported by Cancer Prevention & Research Institute of Texas (CPRIT) Rising Star Award (CPRIT R1307), the National Library of Medicine of the National Institutes of Health under Award Number 2R01LM010681-05, the National Institute of General Medical Sciences under Award Numbers 1R01GM103859 and 1R01GM102282, the National Nature and Science Foundation of China (NSFC 61203378).