RENET: A Deep Learning Approach for Extracting Gene-Disease Associations from Literature

Abstract

Over one million new biomedical articles are published every year. Efficient and accurate text-mining tools are urgently needed to automatically extract knowledge from these articles to support research and genetic testing. In particular, the extraction of gene-disease associations is mostly studied. However, existing text-mining tools for extracting gene-disease associations have limited capacity, as each sentence is considered separately. Our experiments show that the best existing tools, such as BeFree and DTMiner, achieve a precision of 48% and recall rate of 78% at most. In this study, we designed and implemented a deep learning approach, named RENET, which considers the correlation between the sentences in an article to extract gene-disease associations. Our method has significantly improved the precision and recall rate to 85.2% and 81.8%, respectively. The source code of RENET is available at https://bitbucket.org/alexwuhkucs/gda-extraction/src/master/.