Biomedical Text Mining and Ontologies
Biomedical Text Mining and Ontologies is a research topic within Molecular Biology. Science Explorer counts 30k research works in it since 1950. 18.8% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the development and application of biomedical ontologies, text mining, and natural language processing techniques to extract and integrate knowledge from biomedical literature. It encompasses topics such as gene annotation, disease integration, phenotype ontology, data integration, and the use of semantic web technologies in knowledge management.
- Biomedical Ontologies
- Text Mining
- Biomedical Literature
- Gene Annotation
- Disease Integration
- Phenotype Ontology
- Knowledge Management
- Data Integration
- Semantic Web
- Natural Language Processing
- Research works
- 30k fractional, since 1950
- In the world top 10%
- 5.7k per year above
- Top-10% rate
- 18.8% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- -2% the tick is no change
Which countries lead Biomedical Text Mining and Ontologies research?
By volume, the United States and China publish the most (1.3k and 897 works in 2022–2025).
By volume, 2022–2025
- 1 United States 1.3k works
- 2 China 897 works
- 3 Germany 345 works
- 4 India 335 works
- 5 United Kingdom 278 works
- 6 France 250 works
- 7 Italy 171 works
- 8 Canada 139 works
- 9 Japan 133 works
- 10 Spain 121 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Biomedical Text Mining and Ontologies research?
By volume in 2022–2025, University of North Carolina at Chapel Hill publishes the most Biomedical Text Mining and Ontologies research, followed by The University of Texas Health Science Center at Houston and Chinese Academy of Medical Sciences & Peking Union Medical College.
By volume, 2022–2025
- 1 University of North Carolina at Chapel Hill United States 25 works
- 2 The University of Texas Health Science Center at Houston United States 20 works
- 3 Chinese Academy of Medical Sciences & Peking Union Medical College China 19 works
- 4 Harvard University United States 17 works
- 5 University of Michigan United States 17 works
- 6 Stanford University United States 17 works
- 7 Centre National de la Recherche Scientifique France 17 works
- 8 Peking University China 16 works
- 9 University of Illinois Urbana-Champaign United States 16 works
- 10 Dalian University of Technology China 16 works
Who are the leading researchers in Biomedical Text Mining and Ontologies?
The most-cited researchers publishing on Biomedical Text Mining and Ontologies include James Thomas, Wei Liu and J. Michael Cherry.
- 1 James Thomas 13k citations
- 2 Wei Liu 9.7k citations
- 3 J. Michael Cherry 6.4k citations
- 4 Michael Ashburner 5.9k citations
- 5 Hans‐Peter Kriegel 5.7k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Biomedical Text Mining and Ontologies research done?
The largest centres of Biomedical Text Mining and Ontologies research in 2022–2025 are Beijing (China), Paris (France), London (United Kingdom) and Shanghai (China). Among places with at least 20 works in it, it is an unusually large share of all research in Hanover and Bethesda.
Largest cities, 2022–2025
Where it is the local speciality
- HanoverDE · 21.2 works5.3×
- BethesdaUS · 31.8 works5.0×
Location quotient: how much more of its research is in Biomedical Text Mining and Ontologies than the world average.
Where is the best place to study Biomedical Text Mining and Ontologies?
Among universities, judged by research, The University of Texas Health Science Center at Houston, Stanford University and Harvard University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | The University of Texas Health Science Center at Houston United States | 68.9 | 21.4% | 13.0× | 20 | +159.6% |
| 2 | Stanford University United States | 52.0 | 38.5% | 2.7× | 17 | -17.1% |
| 3 | Harvard University United States | 50.8 | 31.3% | 2.6× | 17 | +26.9% |
| 4 | Tsinghua University China | 49.0 | 40.2% | 1.3× | 14 | +55.2% |
| 5 | University of Pennsylvania United States | 45.3 | 29.4% | 2.8× | 13 | +72.3% |
| 6 | University of Illinois Urbana-Champaign United States | 44.8 | 24.4% | 3.6× | 16 | +47.6% |
| 7 | Columbia University United States | 44.7 | 32.1% | 3.3× | 14 | -29.0% |
| 8 | University of North Carolina at Chapel Hill United States | 40.4 | 4.3% | 2.9× | 25 | +190.3% |
| 9 | University of Washington United States | 40.0 | 26.1% | 2.5× | 13 | +42.2% |
| 10 | University of Chinese Academy of Sciences China | 39.6 | 27.5% | 1.1× | 9 | +231.9% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Biomedical Text Mining and Ontologies research growing?
Output in 2018–2022 was 2% lower than in 2013–2017, peaking in 2025. The fastest-growing topics are Biomedical Text Mining and Ontologies.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.