COVID-19 therapy target discovery with context-aware literature mining

TL;DR

This paper introduces a novel literature mining system that uses transfer learning with SciBERT to identify COVID-19 therapy targets from vast scientific publications, outperforming previous methods.

Contribution

The study presents a new embedding generation technique leveraging SciBERT for context-aware literature mining in COVID-19 research, enabling more effective therapy target discovery.

Findings

The proposed method outperforms the FastText baseline significantly.

Manual and quantitative evaluations validate the method's effectiveness.

The system successfully identifies relevant COVID-19 therapy targets.

Abstract

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.