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/.
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This work was supported by Hong Kong ITF Grant ITS/331/17FP and General Research Fund No. 27204518.
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Wu, Y., Luo, R., Leung, H.C.M., Ting, HF., Lam, TW. (2019). RENET: A Deep Learning Approach for Extracting Gene-Disease Associations from Literature. In: Cowen, L. (eds) Research in Computational Molecular Biology. RECOMB 2019. Lecture Notes in Computer Science(), vol 11467. Springer, Cham. https://doi.org/10.1007/978-3-030-17083-7_17
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