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Abstract
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Extracting biological relations from biomedical literature can deliver personalized treatment to individual patients based on their genomic profiles. In this paper, we present a novel sentence-level attention-based deep neural network to predict the semantic relationship between medical entities. We utilize a trans- fer learning based paradigm which considerably improves the prediction performance. The main distinc- tion of the proposed approach is that it relies solely on sentence information, putting aside handcrafted biomedical features. Sentence information is transformed into embedding vectors and improved by the pre-trained embedding models trained on PubMed and PMC papers. Extensive evaluations show that the proposed approach achieves a competitive performance in comparison with the state-of-the-art meth- ods, while do not require any domain-specific biomedical feature. The evaluation data and resources are available at https://github.com/EsmaeilNourani/Deep-GDAE/
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