DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature

TL;DR

This paper introduces DeepVar, an end-to-end deep learning model that effectively recognizes genomic variants in biomedical literature without manual feature engineering, addressing challenges in low-resource NER tasks.

Contribution

The work presents the first successful end-to-end deep learning approach for genomic variant recognition in low-resource biomedical NER tasks, eliminating the need for handcrafted features.

Findings

Achieved promising performance without feature engineering

Demonstrated effectiveness in low-resource genomic variant recognition

Provided insights for similar low-resource NER applications

Abstract

We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work. Significant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domain-specific areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches in such tasks heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the first successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any hand-crafted features or post-processing rules. Our extensive experiments and results may shed light on other similar low-resource NER applications.