Identifying chemical–disease relations (CDR) from biomedical literature could improve
chemical safety and toxicity studies. This article proposes a novel syntactic and semantic …

Mining chemical-induced disease relations embedded in the vast biomedical literature could
facilitate a wide range of computational biomedical applications, such as …

This article describes our work on the BioCreative-V chemical–disease relation (CDR)
extraction task, which employed a maximum entropy (ME) model and a convolutional neural …

Manually curating chemicals, diseases and their relationships is significantly important to
biomedical research, but it is plagued by its high cost and the rapid growth of the biomedical …

Background Automatic extraction of chemical-disease relations (CDR) from unstructured text
is of essential importance for disease treatment and drug development. Meanwhile …

Understanding the relations between chemicals and diseases is crucial in various
biomedical tasks such as new drug discoveries and new therapy developments. While …

Background Automatically understanding chemical-disease relations (CDRs) is crucial in
various areas of biomedical research and health care. Supervised machine learning …

Since identifying relations between chemicals and diseases (CDR) are important for
biomedical research and healthcare, the challenge proposed by BioCreative V requires …

This paper describes the system developed by the UTH-CCB team from the University of
Texas Health Science Center at Houston (UTHealth), for the 2015 BioCreative V shared …

Community-run, formal evaluations and manually annotated text corpora are critically
important for advancing biomedical text-mining research. Recently in BioCreative V, a new …