COVID 19 therapy target discovery with context aware literature mining
The abundance of literature related to the widespread COVID 19 pandemic is beyond manual inspection of a single expert. Development of systems, capable of automatically processing tens of thousands of scientific publications with the aim to enrich existing empirical evidence with literature based associations is challenging and relevant. We propose a system for contextualization of empirical expression data by approximating relations between entities, for which representations were learned from one of the largest COVID 19 related literature corpora. In order to exploit a larger scientific context by transfer learning, we propose a novel embedding generation technique that leverages SciBERT language model pretrained on a large multi domain corpus of scientific publications and fine tuned for domain adaptation on the CORD 19 dataset. The conducted manual evaluation by the medical expert and the quantitative evaluation based on therapy targets identified in the related work suggest that the proposed method can be successfully employed for COVID 19 therapy target discovery and that it outperforms the baseline FastText method by a large margin.
DS