COVID-19 therapy target discovery with context-aware literature mining
This addresses the problem of manual literature review for COVID-19 researchers by providing an automated tool for therapy target discovery, though it is incremental as it builds on existing embedding and transfer learning methods.
The authors tackled the challenge of automatically processing vast COVID-19 literature to discover therapy targets by proposing a context-aware system that enriches empirical data with literature-based associations, and it outperformed the baseline FastText method by a large margin in evaluations.
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.