• Exploiting Latent Semantic Subspaces to Derive Associations for Specific Pharmaceutical Semantics

    5 days ago - By Springer

    Abstract
    State-of-the-art approaches in the field of neural embedding models enable progress in the automatic extraction and prediction of semantic relations between important entities like active substances, diseases, and genes. In particular, the prediction property is making them valuable for important research-related tasks such as hypothesis generation and drug repositioning. A core challenge in the biomedical domain is to have interpretable semantics from NEMs that can distinguish, for instance, between the following two situations: drug x induces disease y and drug x treats disease...
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