https://github.com/ncbi/bioconceptvec
https://github.com/ncbi/bioconceptvec
biomedical-concept-embeddings biomedical-text-mining deep-learning protein-protein-interaction text-mining word-embeddings
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/ncbi/bioconceptvec
- Owner: ncbi
- Created: 2019-05-01T15:10:35.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2020-02-26T05:02:04.000Z (over 6 years ago)
- Last Synced: 2025-07-04T01:36:31.082Z (about 1 year ago)
- Topics: biomedical-concept-embeddings, biomedical-text-mining, deep-learning, protein-protein-interaction, text-mining, word-embeddings
- Language: Jupyter Notebook
- Size: 60.1 MB
- Stars: 41
- Watchers: 5
- Forks: 13
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# BioConceptVec:
creating and evaluating literature-based biomedical concept embeddings on a large scale
[](http://hits.dwyl.com/ncbi-nlp/BioConceptVec)
## Table of contents
* [Text corpora](#text-corpora)
* [Named Entity Recognition (NER) tools](#pubtator)
* [BioConceptVec: embeddings and concept files](#bioconceptvec)
* [Tutorial](#tutorial)
* [Datasets](#dataset)
* [References](#references)
* [Acknowledgments](#acknowledgments)
## Text corpora
We created BioConceptVec using the entire [PubMed](https://www.ncbi.nlm.nih.gov/pubmed/). The texts were split and tokenized using [NLTK](https://www.nltk.org/). We also lowercased all the words.
## Using PubTator for annotating concepts in the PubMed
We employed [PubTator](https://www.ncbi.nlm.nih.gov/research/pubtator/) to annotate biomedical concepts in the PubMed. It covers genes, mutations, chemicals, diseases and cellines. The trained embeddings contain over 400,000 concepts.
## BioConceptVec: embeddings and concept files
We release four versions of BioConceptVec (cbow, skip-gram, glove and fastText). For each version, we make both the **embedding**(contains concepts and other words) in binary format and the **concept-only** file in json format available.
1. **BioConceptVec cbow:** [embedding](https://ftp.ncbi.nlm.nih.gov/pub/lu/BioConceptVec/bioconceptvec_word2vec_cbow.bin) (2.4GB) and [concept-only](https://ftp.ncbi.nlm.nih.gov/pub/lu/BioConceptVec/concept_cbow.json) (798MB).
2. **BioConceptVec skip-gram:** [embedding](https://ftp.ncbi.nlm.nih.gov/pub/lu/BioConceptVec/bioconceptvec_word2vec_skipgram.bin) (2.4GB) and [concept-only](https://ftp.ncbi.nlm.nih.gov/pub/lu/BioConceptVec/concept_skip.json) (812MB).
3. **BioConceptVec glove:** [embedding](https://ftp.ncbi.nlm.nih.gov/pub/lu/BioConceptVec/bioconceptvec_glove.bin) (2.4GB) and [concept-only](https://ftp.ncbi.nlm.nih.gov/pub/lu/BioConceptVec/concept_glove.json) (835MB).
4. **BioConceptVec fastText:** [embedding](https://ftp.ncbi.nlm.nih.gov/pub/lu/BioConceptVec/bioconceptvec_fasttext.bin) (2.4GB) and [concept-only](https://ftp.ncbi.nlm.nih.gov/pub/lu/BioConceptVec/concept_fast.json) (813MB).
## Tutorial
You can find [this tutorial](https://github.com/ncbi-nlp/BioConceptVec/blob/master/bioconcept_tutorial.ipynb) on how to use BioConceptVec (for both embedding and concept-only files) for a quick start.
## Datasets
We also make all the 9 evaluation datasets publicly available. It covers 4 applications:
1. [**Drug-Gene interactions**](https://github.com/ncbi-nlp/BioConceptVec/tree/master/datasets/drug_gene_interactions). The dataset contains (1) ID: the instance ID, (2) num_of_genes: the number of genes for this instance, (3) pos_rel_genes: the IDs of related genes, and (4) neg_rel_genes: the IDs of unrelated genes.
2. [**Gene-Gene interactions**](https://github.com/ncbi-nlp/BioConceptVec/tree/master/datasets/gene_gene_interactions). 5 datasets on gene-gene interactions have the same format as above.
3. [**Protein-Protein interaction**](https://github.com/ncbi-nlp/BioConceptVec/tree/master/datasets/protein_protein_interactions). It contains two datasets: (1) combined: protein-protein interactions created based on STRING combined scores and (2) exp700: protein-protein interactions created based on STRING experimental scores over 700. Both datasets contain train, valid and test files. The file contains (1) query: query protein ID, (2) subject: subject protein ID, (3) score: STRING score and (4) label: whether it is a protein-protein interaction.
4. [**Drug-Drug interaction**](https://github.com/ncbi-nlp/BioConceptVec/tree/master/datasets/drug_drug_interactions). This dataset is from [Drug-Drug interaction semeval-2013](https://www.cs.york.ac.uk/semeval-2013/task9/). Please see the details there.
## References
When using our resources, please cite the following papers:
Chen, Q., Lee, K., Yan, S., Kim, S., Wei, C. H., & Lu, Z. (2019). [BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale](https://arxiv.org/ftp/arxiv/papers/1912/1912.10846.pdf). To appear in PLOS Computational Biology.
## Acknowledgments
This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of Medicine.