Guides

• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Getting Started in Text Mining - 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 - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.
• Biomedical Literature Mining - 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.

Challenges

• NLTK tutorials - 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.

Video Lectures and Online Courses

• Coursera - Foundations of mining non-structured 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
• VIB text mining and curation training - This training workshop happenened in 2013 but the slides are still online.

• open science

Journals

• arXiv
• Database - Its subtitle is "The Journal of Biological Databases and Curation". Open access.
• NAR - Nucleic Acids Research. Has a broad biomolecular focus but is particularly notable for its annual database issue.
• JAMIA - The Journal of the American Medical Informatics Association. Concerns "articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy".
• JBI - The Journal of Biomedical Informatics. Not open access by default, though it does have an open-access "X" version.
• Scientific Data - An open-access Springer Nature journal publishing "descriptions of scientifically valuable datasets, and research that advances the sharing and reuse of scientific data".
• Scientific Data - An open-access Springer Nature journal publishing "descriptions of scientifically valuable datasets, and research that advances the sharing and reuse of scientific data".

Conferences and Other Events

• ACM-BCB - The ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. Held annually since 2010.
• BIBM - The IEEE International Conference on Bioinformatics and Biomedicine.
• ISMB - The International Conference on Intelligent Systems for Molecular Biology is an annual conference hosted by the International Society for Computational Biology since 1993. Much of its focus has concerned bioinformatics and computational biology without an explicit clinical focus, though it has included an increasing amount of text mining content (e.g., the 2019 meeting included a [full-day special session on Text Mining for Biology and Healthcare](http://cosi.iscb.org/wiki/TextMining:Home)). The meeting is combined with that of the European Conference on Computational Biology (ECCB) on odd-numbered years.
• PSB - The Pacific Symposium on Biocomputing.

Challenges

• BioASQ - Challenges on biomedical semantic indexing and question answering. Challenges and workshops held annually since 2013.
• BioCreAtIvE workshop - These workshops have been organized since 2004, with BioCreative VI happening February 2017 and the [BioCreative/OHNLP Challenge](https://sites.google.com/view/ohnlp2018/home) held in 2018. See [Datasets](#datasets) below.
• SemEval workshop - Tasks and evaluations in computational semantic analysis. Tasks vary by year but frequently cover scientific and/or biomedical language, e.g. the [SemEval-2019 Task 12 on Toponym Resolution in Scientific Papers](https://competitions.codalab.org/competitions/19948).
• EHR DREAM Challenge - Held along with several other [more bioinformatics-focused challenges](http://dreamchallenges.org/), this challenge opened in October 2019 and focuses on using electronic health record data to predict patient mortality. Uses a synthetic data set rather than real EHR contents.

Video Lectures and Online Courses

• Biopython - [paper](http://dx.doi.org/10.1093/bioinformatics/btp163) - [code](https://github.com/biopython/biopython) - Python tools primarily intended for bioinformatics and computational molecular biology purposes, but also a convenient way to obtain data, including documents/abstracts from PubMed (see Chapter 9 of the documentation).
• Med7 - [paper](https://arxiv.org/abs/2003.01271) - [code](https://github.com/kormilitzin/med7) - a Python package and model (for use with spaCy) for doing NER with medication-related concepts.

Biomedical Text Sources

• OHSUMED - [paper](https://dl.acm.org/citation.cfm?id=188557) - 348,566 MEDLINE entries (title and sometimes abstract) from between 1987 and 1991. Includes MeSH labels. Primarily of historical significance.
• PubMed Central Open Access Subset - A set of PubMed Central articles usable under licenses other than traditional copyright, though the exact licenses vary by publication and source. Articles are available as PDF and XML.

Language Models

• UMLS Terminology Services (UTS) account

Annotated Text Data

• SPL-ADR-200db - [paper](https://www.nature.com/articles/sdata20181) - A pilot dataset containing standardised information, and annotations of occurence in text, about ~5,000 known adverse reactions for 200 FDA-approved drugs.
• BioCreAtIvE V CDR Task Corpus (BC5CDR) - [paper](https://academic.oup.com/database/article/doi/10.1093/database/baw068/2630414) - 1,500 articles (title and abstract) published in 2014 or later, annotated for 4,409 chemicals, 5,818 diseases and 3116 chemical–disease interactions. Requires registration.
• BioCreative VI CHEMPROT Corpus - [paper](https://pdfs.semanticscholar.org/eed7/81f498b563df5a9e8a241c67d63dd1d92ad5.pdf) - >2,400 articles annotated with chemical-protein interactions of a variety of relation types. Requires registration.
• n2c2 (formerly i2b2) Data - The Department of Biomedical Informatics (DBMI) at Harvard Medical School manages data for the National NLP Clinical Challenges and the Informatics for Integrating Biology and the Bedside challenges running since 2006. They require registration before access and use. Datasets include a variety of topics. See the [list of data challenges](https://portal.dbmi.hms.harvard.edu/data-challenges/) for individual descriptions.
• NCBI Disease Corpus - [paper](https://www.sciencedirect.com/science/article/pii/S1532046413001974) - A corpus of 793 biomedical abstracts annotated with names of diseases and related concepts from MeSH and [OMIM](https://omim.org/).
• Word Sense Disambiguation (WSD) - [paper](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-223) - 203 ambiguous words and 37,888 automatically extracted instances of their use in biomedical research publications. Requires UTS account.
• Clinical Questions Collection - also known as CQC or the Iowa collection, these are several thousand questions posed by physicians during office visits along with the associated answers.
• BioNLP ST 2013 datasets - data from six shared tasks, though some may not be easily accessible; try the CG task set (BioNLP2013CG) for extensive entity and event annotations.
• BioScope - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2586758/) - a corpus of sentences from medical and biological documents, annotated for negation, speculation, and linguistic scope.
• BioRED - [paper](https://arxiv.org/abs/2204.04263) - a set of >6.5K biomedical relation annotations, plus labels for novel findings.
• BioCreAtIvE V CDR Task Corpus (BC5CDR) - [paper](https://academic.oup.com/database/article/doi/10.1093/database/baw068/2630414) - 1,500 articles (title and abstract) published in 2014 or later, annotated for 4,409 chemicals, 5,818 diseases and 3116 chemical–disease interactions. Requires registration.
• BioCreAtIvE 2 - [paper](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2008-9-s2-s1) - 15,000 sentences (10,000 training and 5,000 test, different from the first corpus) annotated for protein and gene names. 542 abstracts linked to EntrezGene identifiers. A variety of research articles annotated for features of protein–protein interactions.
• PubTator Central datasets - [paper](https://academic.oup.com/nar/article/47/W1/W587/5494727) - Accessible through a RESTful API or FTP download. Includes annotations for >29 million abstracts and ∼3 million full text documents.
• BioNLP ST 2013 datasets - data from six shared tasks, though some may not be easily accessible; try the CG task set (BioNLP2013CG) for extensive entity and event annotations.

Protein-protein Interaction Annotated Corpora

• BioInfer - [paper](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-50) - 1,100 sentences from biomedical research abstracts annotated for relationships (including PPI), named entities, and syntactic dependencies. [Additional information and download links are here.](http://mars.cs.utu.fi/BioInfer/)
• BioC format - berlin.de) and were originally derived from the original sets by a [group at Turku University](http://mars.cs.utu.fi/PPICorpora/).
• AIMed - [paper](https://www.ncbi.nlm.nih.gov/pubmed/15811782) - 225 MEDLINE abstracts annotated for PPI.
• BioC-BioGRID - [paper](https://academic.oup.com/database/article/doi/10.1093/database/baw147/2884890) - 120 full text articles annotated for PPI and genetic interactions. Used in the BioCreative V BioC task.
• HPRD50 - [paper](https://academic.oup.com/bioinformatics/article/23/3/365/236564) - 50 scientific abstracts referenced by the Human Protein Reference Database, annotated for PPI.
• IEPA - [paper](http://psb.stanford.edu/psb-online/proceedings/psb02/abstracts/p326.html) - 486 sentences from biomedical research abstracts annotated for pairs of co-occurring chemicals, including proteins (hence, PPI annotations).
• LLL - [paper](https://www.semanticscholar.org/paper/Learning-Language-in-Logic-Genic-Interaction-Nedellec/0863a9d71955341b7e1a6a6877d44d4f0bb22671) - 77 sentences from research articles about the bacterium _Bacillus subtilis_, annotated for protein–gene interactions (so, fairly close to PPI annotations). [Additional information is here.](http://genome.jouy.inra.fr/texte/LLLchallenge/#task1)

Other Datasets

• Columbia Open Health Data - [paper](https://www.nature.com/articles/sdata2018273) - A database of prevalence and co-occurrence frequencies of conditions, drugs, procedures, and patient demographics extracted from electronic health records. Does not include original record text.
• Comparative Toxicogenomics Database - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323936/) - A database of manually curated associations between chemicals, gene products, phenotypes, diseases, and environmental exposures. Useful for assembling ontologies of the related concepts, such as types of chemicals.
• MIMIC-III - [paper](https://www.nature.com/articles/sdata201635) - Deidentified health data from ~60,000 intensive care unit admissions. Requires completion of an online training course (CITI training) and acceptance of a data use agreement prior to use.
• MIMIC-CXR - The MIMIC Chest X-Ray database. Contains more than 377,000 radiographic images and accompanying free-text radiology reports. As with MIMIC-III, requires acceptance of a data use agreement.
• UMLS Knowledge Sources - [reference manual](https://www.ncbi.nlm.nih.gov/books/NBK9676/) - A large and comprehensive collection of biomedical terminology and identifiers, as well as accompanying tools and scripts. Depending on your purposes, the single file MRCONSO.RRF may be sufficient, as this file contains unique identifiers and names for all concepts in the UMLS Metathesaurus. See also the Ontologies and Controlled Vocabularies section below.
• MIMIC-IV - An update to MIMIC-III's multimodal patient data, now covering more recent years of admissions, plus a new data structure, emergency department records, and links to MIMIC-CXR images.
• eICU Collaborative Research Database - [paper](https://www.nature.com/articles/sdata2018178) - a database of observations from more than 200 thousand intensive care unit admissions, with consistent structure. Requires registration, training course completion, and data use agreement.

Repos for Specific Datasets

• cTAKES - [paper](https://academic.oup.com/jamia/article/17/5/507/830823) - [code](https://github.com/apache/ctakes) - A system for processing the text in electronic medical records. Widely used and open source.
• CLAMP - [paper](https://academic.oup.com/jamia/article/25/3/331/4657212) - A natural language processing toolkit intended for use with the text in clinical reports. Check out their [live demo](https://clamp.uth.edu/clampdemo.php) first to see what it does. Usable at no cost for academic research.
• DNorm - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3810844/) - A method for disease normalization, i.e., linking mentions of disease names and acronyms to unique concept identifiers. Downloadable version includes the NCBI Disease Corpus and BC5CDR (see Annotated Text Data below).
• TaggerOne - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5018376/) - Performs concept normalization (see also DNorm above). Can be trained for specific concept types and can perform NER independent of other normalization functions.

Annotation Tools

• brat - [paper](https://www.aclweb.org/anthology/E12-2021/) - [code](https://github.com/nlplab/brat) - The brat rapid annotation tool. Supports producing text annotations visually, through the browser. Not subject specific; appropriate for many annotation projects. Visualization is based on that of the [_stav_ tool](https://github.com/nlplab/stav/).
• MedTator - [paper](https://academic.oup.com/bioinformatics/article-abstract/38/6/1776/6496915) - [code](https://github.com/OHNLP/MedTator) - An annotation tool designed to have minimal dependencies.

Text Embeddings

• This paper from Hongfang Liu's group at Mayo Clinic - trained embeddings may be appropriate for your needs, especially as training domain-specific embeddings can be computationally intensive.

Word Embeddings

• BioASQword2vec - [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 - [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

• PubMedBERT - [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.
• Flair embeddings from PubMed - 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.

Other Datasets

• Disease Ontology - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383880/) - An ontology of human diseases. Has cross-links to MeSH, ICD, NCI Thesaurus, SNOMED, and OMIM. Public domain. Available on [GitHub](https://github.com/DiseaseOntology/HumanDiseaseOntology) and on the [OBO Foundry](http://www.obofoundry.org/ontology/doid.html).
• RxNorm - [paper](https://academic.oup.com/jamia/article/18/4/441/734170) - Normalized names for clinical drugs and drug packs, with combined ingredients, strengths, and form, and assigned types from the Semantic Network (see below). Released monthly.
• SPECIALIST Lexicon - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2247735/) - A general English lexicon that includes many biomedical terms. Updated yearly since 1994 and still updated as of 2019. Part of UMLS but does not require UTS account to download.
• UMLS Metathesaurus - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC308795/) - Mappings between >3.8 million concepts, 14 million concept names, and >200 sources of biomedical vocabulary and identifiers. It's big. It may help to prepare a subset of the Metathesaurus with the [MetamorphoSys installation tool](https://www.nlm.nih.gov/research/umls/implementation_resources/metamorphosys/help.html) but we're still talking about ~30 Gb of disk space required for the 2019 release. [See the manual here](https://www.ncbi.nlm.nih.gov/books/NBK9684/). Requires UTS account.
• UMLS Semantic Network - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447396/) - Lists of 133 semantic types and 54 semantic relationships covering biomedical concepts and vocabulary. Is the Metathesaurus too complex for your needs? Try this. Does not require UTS account to download.

Other Datasets

• data model
• BioUML - [paper](https://academic.oup.com/nar/article/47/W1/W225/5498754) - An architecture for biomedical data analysis, integration, and visualization. Conceptually based on the visual modeling language [UML](https://www.uml.org/what-is-uml.htm).