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<div align="center">
<img src="https://github.com/caufieldjh/awesome-bioie/blob/master/images/abie_head.png" alt="Awesome BioIE Logo"/>
<img src="https://github.com/caufieldjh/awesome-bioie/blob/main/images/abie_head.png" alt="Awesome BioIE Logo"/>
<br>
<a href="https://awesome.re">
<img src="https://awesome.re/badge-flat2.svg" alt="Awesome">
@@ -12,6 +12,8 @@
What is BioIE? It includes any effort to extract structured information from _unstructured_ (or, at least inconsistently structured) biological, clinical, or other biomedical data. The data source is often some collection of text documents written in technical language. If the resulting information is verifiable and consistent across sources, we may then consider it _knowledge_. Extracting information and producing knowledge from bio data requires adaptations upon methods developed for other types of unstructured data.
BioIE has undergone massive changes since the introduction of language models like BERT and the more recently created Large Language Models (LLMs; e.g., GPT-3/4, LLAMA2/3, Gemini, etc).
Resources included here are preferentially those available at no monetary cost and limited license requirements. Methods and datasets should be publicly accessible and actively maintained.
See also [awesome-nlp](https://github.com/keon/awesome-nlp), [awesome-biology](https://github.com/raivivek/awesome-biology) and [Awesome-Bioinformatics](https://github.com/danielecook/Awesome-Bioinformatics).
@@ -33,10 +35,7 @@ _Please read the [contribution guidelines](contributing.md) before contributing.
* [Repos for Specific Datasets](#repos-for-specific-datasets)
* [Tools, Platforms, and Services](#tools-platforms-and-services)
* [Annotation Tools](#annotation-tools)
* [Techniques](#techniques)
* [Text Embeddings](#text-embeddings)
* [Word Embeddings](#word-embeddings)
* [Language Models](#language-models)
* [Techniques and Models](#techniques-and-models)
* [Datasets](#datasets)
* [Biomedical Text Sources](#biomedical-text-sources)
* [Annotated Text Data](#annotated-text-data)
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## Research Overviews
### LLMs in Biomedical IE
* [Large language models in healthcare: A comprehensive benchmark](http://dx.doi.org/10.1101/2024.04.24.24306315) - a statistical and human evaluation of sixteen different LLMs applied to medical language tasks.
* [Assessing the research landscape and clinical utility of large language models: a scoping review](https://doi.org/10.1186/s12911-024-02459-6) - a high-level review of LLM applications in medicine as of March 2024.
* [Ethical and regulatory challenges of large language models in medicine](https://doi.org/10.1016/s2589-7500(24)00061-x) - a review of ethical issues arising from applications of LLMs in biomedicine.
* [On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜](http://dx.doi.org/10.1145/3442188.3445922) - a frequently referenced but still relevant work concerning the roles, applications, and risks of language models.
### Pre-LLM Overviews
* [Biomedical Informatics on the Cloud: A Treasure Hunt for Advancing Cardiovascular Medicine](https://www.ahajournals.org/doi/full/10.1161/CIRCRESAHA.117.310967) - An overview of how BioIE and bioinformatics workflows can be applied to questions in cardiovascular health and medicine research.
* [Clinical information extraction applications: A literature review](https://www.sciencedirect.com/science/article/pii/S1532046417302563) - A review of clinical IE papers published as of September 2016. From Mayo Clinic group (see below).
* [Literature Based Discovery: Models, methods, and trends](https://www.sciencedirect.com/science/article/pii/S1532046417301909) - A review of Literature Based Discovery (LBD), or the philosophy that meaningful connections may be found between seemingly unrelated scientific literature.
@@ -60,10 +66,6 @@ _Please read the [contribution guidelines](contributing.md) before contributing.
## Groups Active in the Field
* [Boston Children's Hospital Natural Language Processing Laboratory](http://www.childrenshospital.org/research/labs/natural-language-processing-laboratory) - Led by Dr. Guergana Savova, formerly at Mayo Clinic and the Apache cTAKES project.
* [BD2K](https://commonfund.nih.gov/bd2k) - The U.S. National Institutes of Health (NIH) funded 13 Centers of Excellence through their Big Data to Knowledge (BD2K) program, several of which developed tools and resources for BioIE.
* [HeartBD2K](http://www.heartbd2k.org/) - Based at University of California, Los Angeles (UCLA). Led by Dr. Peipei Ping.
* [KnowEng](https://knoweng.org/about/people/) - Based an University of Illinois at Urbana-Champaign (UIUC). Led by Dr. Jiawei Han.
* [Mobilize](http://mobilize.stanford.edu/) - Based at Stanford. Led by Dr. Scott Delp.
* [Brown Center for Biomedical Informatics](https://www.brown.edu/academics/medical/about-us/research/centers-institutes-and-programs/biomedical-informatics/) - Based at Brown University and directed by Dr. Neil Sarkar, whose research group works on topics in clinical NLP and IE.
* [Center for Computational Pharmacology NLP Group](http://compbio.ucdenver.edu/Hunter_lab/CCP_website/index.html) - based at University of Colorado, Denver and led by Larry Hunter - [see their GitHub repos here.](https://github.com/UCDenver-ccp)
* Groups at U.S. National Institutes of Health (NIH) / National Library of Medicine (NLM):
@@ -123,15 +125,16 @@ Some events in BioIE are organized around formal tasks and challenges in which g
## Tutorials
The field changes rapidly enough that tutorials any older than a few years are missing crucial details. A few more recent educational resources are listed below. A good foundational understanding of text mining techniques is very helpful, as is some basic experience with the Python and or R languages. Starting with the [NLTK tutorials](https://www.nltk.org/book/) and then trying out the tutorials for the [Flair framework](https://github.com/zalandoresearch/flair) will provide excellent examples of natural language processing, text mining, and modern machine learning-driven methods, all in Python. Most of the examples don't include anything biomedical, however, so the best option may be to learn by doing.
The field changes rapidly enough that tutorials any older than a few years are missing crucial details. A few more recent educational resources are listed below. A good foundational understanding of text mining techniques is very helpful, as is some basic experience with the Python and or R languages. The best option may be to learn by doing.
### Guides
### LLM Guides
_TBD - watch this space!_
### Pre-LLM Guides, Lectures, and Courses
* [Getting Started in Text Mining](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.0040020) - A brief introduction to bio-text mining from Cohen and Hunter. More than ten years old but still quite relevant. See also an [earlier paper by the same authors](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1702322/).
* [Biomedical Literature Mining](https://link.springer.com/book/10.1007/978-1-4939-0709-0) - A (non-free) volume of Methods in Molecular Biology from 2014. Chapters covers introductory principles in text mining, applications in the biological sciences, and potential for use in clinical or medical safety scenarios.
### Video Lectures and Online Courses
* [Coursera - Foundations of mining non-structured medical data](https://www.coursera.org/learn/mining-medical-data) - About three hours worth of video lectures on working with medical data of various types and structures, including text and image data. Appears fairly high-level and intended for beginners.
* [JensenLab text mining exercises](https://jensenlab.org/training/textmining/)
* [VIB text mining and curation training](https://www.bits.vib.be/training-list/111-bits/training/previous-trainings/183-text-mining) - This training workshop happenened in 2013 but the slides are still online.
@@ -173,20 +176,13 @@ The field changes rapidly enough that tutorials any older than a few years are m
[Back to Top](#contents)
## Techniques
## Techniques and Models
### Text Embeddings
### Large Language Models
[This paper from Hongfang Liu's group at Mayo Clinic](https://www.sciencedirect.com/science/article/pii/S1532046418301825) demonstrates how text embeddings trained on biomedical or clinical text can, but don't always, perform better on biomedical natural language processing tasks. That being said, pre-trained embeddings may be appropriate for your needs, especially as training domain-specific embeddings can be computationally intensive.
_TBD - watch this space!_
### Word Embeddings
* [BioASQword2vec](http://bioasq.org/news/bioasq-releases-continuous-space-word-vectors-obtained-applying-word2vec-pubmed-abstracts) - [paper](http://bioasq.lip6.fr/info/BioASQword2vec/) - Qord embeddings derived from biomedical text (>10 million PubMed abstracts) using the popular [word2vec](https://code.google.com/archive/p/word2vec/) tool.
* [BioWordVec](https://figshare.com/articles/Improving_Biomedical_Word_Embeddings_with_Subword_Information_and_MeSH_Ontology/6882647) - [paper](https://www.nature.com/articles/s41597-019-0055-0) - [code](https://github.com/ncbi-nlp/BioWordVec) - Word embeddings derived from biomedical text (>27 million PubMed titles and abstracts), including subword embedding model based on MeSH.
### Language Models
#### BERT models
### BERT models
* [BioBERT](https://github.com/naver/biobert-pretrained) - [paper](https://arxiv.org/abs/1901.08746) - [code](https://github.com/dmis-lab/biobert) - A PubMed and PubMed Central-trained version of the [BERT language model](https://arxiv.org/abs/1810.04805).
* ClinicalBERT - Two language models trained on clinical text have similar names. Both are BERT models trained on the text of clinical notes from the MIMIC-III dataset.
* [Alsentzer et al Clinical BERT](https://github.com/EmilyAlsentzer/clinicalBERT) - [paper](https://www.aclweb.org/anthology/W19-1909/)
@@ -195,12 +191,17 @@ The field changes rapidly enough that tutorials any older than a few years are m
* [BlueBERT](https://github.com/ncbi-nlp/bluebert) - [paper](https://arxiv.org/abs/1906.05474) - A BERT model pre-trained on PubMed text and MIMIC-III notes.
* [PubMedBERT](https://microsoft.github.io/BLURB/models.html) - [paper](https://arxiv.org/abs/2007.15779) - A BERT model trained from scratch on PubMed, with versions trained on abstracts+full texts and on abstracts alone.
#### GPT models
### GPT-2 models
* [BioGPT](https://github.com/microsoft/BioGPT) - [paper](https://doi.org/10.1093/bib/bbac409) - A GPT-2 model pre-trained on 15 million PubMed abstracts, along with fine-tuned versions for several biomedical tasks.
#### Other models
### Other models
* [Flair embeddings from PubMed](https://github.com/zalandoresearch/flair/pull/519) - A language model available through the Flair framework and embedding method. Trained over a 5% sample of PubMed abstracts until 2015, or > 1.2 million abstracts in total.
### Text Embeddings
* [This paper from Hongfang Liu's group at Mayo Clinic](https://www.sciencedirect.com/science/article/pii/S1532046418301825) demonstrates how text embeddings trained on biomedical or clinical text can, but don't always, perform better on biomedical natural language processing tasks. That being said, pre-trained embeddings may be appropriate for your needs, especially as training domain-specific embeddings can be computationally intensive.
* [BioASQword2vec](http://bioasq.org/news/bioasq-releases-continuous-space-word-vectors-obtained-applying-word2vec-pubmed-abstracts) - [paper](http://bioasq.lip6.fr/info/BioASQword2vec/) - Qord embeddings derived from biomedical text (>10 million PubMed abstracts) using the popular [word2vec](https://code.google.com/archive/p/word2vec/) tool.
* [BioWordVec](https://figshare.com/articles/Improving_Biomedical_Word_Embeddings_with_Subword_Information_and_MeSH_Ontology/6882647) - [paper](https://www.nature.com/articles/s41597-019-0055-0) - [code](https://github.com/ncbi-nlp/BioWordVec) - Word embeddings derived from biomedical text (>27 million PubMed titles and abstracts), including subword embedding model based on MeSH.
[Back to Top](#contents)
## Datasets
@@ -281,3 +282,6 @@ Do you need a [data model](https://en.wikipedia.org/wiki/Data_model)? If you are
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