# tmVar 3.0: an improved variant concept recognition and normalization tool

arXiv: https://arxiv.org/abs/2204.03637 · HTML: https://mlsanity.com/paper/2204.03637/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [LitGen: Genetic Literature Recommendation Guided by Human Explanations](https://mlsanity.com/paper/1909.10699/) (2019)
- [DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature](https://mlsanity.com/paper/2006.08338/) (2020)

## Built on by (most cited first, 10 of 10)

- [SciMON: Scientific Inspiration Machines Optimized for Novelty](https://mlsanity.com/paper/2305.14259/) (2023)
- [BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing](https://mlsanity.com/paper/2206.15076/) (2022)
- [PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge](https://mlsanity.com/paper/2401.11048/) (2024)
- [AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning](https://mlsanity.com/paper/2211.16944/) (2022)
- [HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools](https://mlsanity.com/paper/2402.12372/) (2024)
- [BELB: a Biomedical Entity Linking Benchmark](https://mlsanity.com/paper/2308.11537/) (2023)
- [EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical and Life Science Text](https://mlsanity.com/paper/2304.07805/) (2023)
- [Effective Multi-Task Learning for Biomedical Named Entity Recognition](https://mlsanity.com/paper/2507.18542/) (2025)
- [Fine-Tuning Models for Biomedical Relation Extraction](https://mlsanity.com/paper/2609.20169/) (2026)
- [BELXTR: Biomedical Entity Linking via Contextualized Token Retrieval](https://mlsanity.com/paper/2609.25859/) (2026)

## Abstract (arXiv)

Previous studies have shown that automated text-mining tools are becoming increasingly important for successfully unlocking variant information in scientific literature at large scale. Despite multiple attempts in the past, existing tools are still of limited recognition scope and precision. We propose tmVar 3.0: an improved variant recognition and normalization tool. Compared to its predecessors, tmVar 3.0 is able to recognize a wide spectrum of variant related entities (e.g., allele and copy number variants), and to group different variant mentions belonging to the same concept in an article for improved accuracy. Moreover, tmVar3 provides additional variant normalization options such as allele-specific identifiers from the ClinGen Allele Registry. tmVar3 exhibits a state-of-the-art performance with over 90% accuracy in F-measure in variant recognition and normalization, when evaluated on three independent benchmarking datasets. tmVar3 is freely available for download. We have also processed the entire PubMed and PMC with tmVar3 and released its annotations on our FTP. Availability: ftp://ftp.ncbi.nlm.nih.gov/pub/lu/tmVar3