# BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale

arXiv: https://arxiv.org/abs/1912.10846 · HTML: https://mlsanity.com/paper/1912.10846/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2019.

## Built on by (most cited first, 8 of 8)

- [Benchmarking large language models for biomedical natural language processing applications and recommendations](https://mlsanity.com/paper/2305.16326/) (2023)
- [Bioformer: an efficient transformer language model for biomedical text mining](https://mlsanity.com/paper/2302.01588/) (2023)
- [LitMC-BERT: transformer-based multi-label classification of biomedical literature with an application on COVID-19 literature curation](https://mlsanity.com/paper/2204.08649/) (2022)
- [A Hybrid Approach to Measure Semantic Relatedness in Biomedical Concepts](https://mlsanity.com/paper/2101.10196/) (2021)
- [TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora](https://mlsanity.com/paper/2103.11029/) (2021)
- [Predicting drug-gene relations via analogy tasks with word embeddings](https://mlsanity.com/paper/2406.00984/) (2024)
- [A reproducible experimental survey on biomedical sentence similarity: a string-based method sets the state of the art](https://mlsanity.com/paper/2205.08740/) (2022)
- [BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels](https://mlsanity.com/paper/2604.15591/) (2026)

## Abstract (arXiv)

Capturing the semantics of related biological concepts, such as genes and mutations, is of significant importance to many research tasks in computational biology such as protein-protein interaction detection, gene-drug association prediction, and biomedical literature-based discovery. Here, we propose to leverage state-of-the-art text mining tools and machine learning models to learn the semantics via vector representations (aka. embeddings) of over 400,000 biological concepts mentioned in the entire PubMed abstracts. Our learned embeddings, namely BioConceptVec, can capture related concepts based on their surrounding contextual information in the literature, which is beyond exact term match or co-occurrence-based methods. BioConceptVec has been thoroughly evaluated in multiple bioinformatics tasks consisting of over 25 million instances from nine different biological datasets. The evaluation results demonstrate that BioConceptVec has better performance than existing methods in all tasks. Finally, BioConceptVec is made freely available to the research community and general public via https://github.com/ncbi-nlp/BioConceptVec.