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Topic · Molecular Biology

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. 10.2% 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%
3.1k
per year above
Top-10% rate
10.2%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+15%
the tick is no change
Largest producer
United States
32% of the 10 largest, 2022-now
Biomedical Text Mining and Ontologies

Which countries lead Biomedical Text Mining and Ontologies research?

By volume, the United States and China publish the most (1.5k and 1k works in 2022-now).

By volume, 2022-now

  1. 1 United States 1.5k works
  2. 2 China 1k works
  3. 3 Germany 438 works
  4. 4 India 399 works
  5. 5 United Kingdom 339 works
  6. 6 France 277 works
  7. 7 Italy 202 works
  8. 8 Canada 163 works
  9. 9 Japan 151 works
  10. 10 Spain 147 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.

United States: 32.4%China: 22.4%Germany: 9.4%India: 8.5%6 others listed: 27.3%32%largest
United States1,517 · 32.4%China1,049 · 22.4%Germany438 · 9.4%India399 · 8.5%6 others listed1,279 · 27.3%

Shares of the rows listed above, not of the whole node.

Which institutions lead Biomedical Text Mining and Ontologies research?

By volume in 2022-now, Centre National de la Recherche Scientifique publishes the most Biomedical Text Mining and Ontologies research, followed by Harvard University and National Institutes of Health.

By volume, 2022-now

  1. 1 Centre National de la Recherche ScientifiqueFrance 64 works
  2. 2 Harvard UniversityUnited States 49 works
  3. 3 National Institutes of HealthUnited States 37 works
  4. 4 Chinese Academy of SciencesChina 36 works
  5. 5 University of North Carolina at Chapel HillUnited States 35 works
  6. 6 Leibniz AssociationGermany 34 works
  7. 7 InsermFrance 27 works
  8. 8 Stanford UniversityUnited States 26 works
  9. 9 Peking UniversityChina 22 works
  10. 10 Chinese Academy of Medical Sciences & Peking Union Medical CollegeChina 21 works

Who are the leading researchers in Biomedical Text Mining and Ontologies?

The most-cited researchers publishing on Biomedical Text Mining and Ontologies include David Moher, Douglas G. Altman and Yoshua Bengio.

  1. 1 David Moher Canada 898k citations
  2. 2 Douglas G. Altman United Kingdom 862k citations
  3. 3 Yoshua Bengio Canada 476k citations
  4. 4 Rob Knight United States 468k citations
  5. 5 George Davey Smith United Kingdom 435k citations
  6. 6 Richard M. Durbin United Kingdom 410k citations
  7. 7 Mark J. Daly United States 403k citations
  8. 8 Peer Bork Germany 396k citations
  9. 9 Robert John Tibshirani United States 396k citations
  10. 10 Julian P. T. Higgins United Kingdom 390k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Biomedical Text Mining and Ontologies?

The most cited paper in Biomedical Text Mining and Ontologies is "Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support" (Paul A. Harris, ROBERT J. TAYLOR, Robert J. Thielke et al., 2008), with 53,362 citations. Next come "Gene Ontology: tool for the unification of biology" (45,425) and "STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets" (19,535).

Most cited papers
#PaperReleasedCitations
1 Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support Paul A. Harris, ROBERT J. TAYLOR, Robert J. Thielke et al. · Journal of Biomedical Informatics Open access Oct 2008 53,362
2 Gene Ontology: tool for the unification of biology Michael Ashburner, Catherine A. Ball, Judith A. Blake et al. · Nature Genetics Open access May 2000 45,425
3 STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets Damian Szklarczyk, Annika L. Gable, David B. Lyon et al. · Nucleic Acids Research Open access 16 Nov 2018 19,535
4 Haploview: analysis and visualization of LD and haplotype maps Jeffrey C. Barrett, Ben N. Fry, Julian Maller et al. · Bioinformatics Open access 5 Aug 2004 14,838
5 A translation approach to portable ontology specifications Thomas Olaf Gruber · Knowledge Acquisition Open access Jun 1993 12,728
All 98 most cited papers, with filters and sorting

Where is Biomedical Text Mining and Ontologies research done?

The largest centres of Biomedical Text Mining and Ontologies research in 2022-now are Beijing (China), London (United Kingdom), Paris (France) 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-now

  1. 1 BeijingChina 223 works
  2. 2 LondonUnited Kingdom 102 works
  3. 3 ParisFrance 97 works
  4. 4 ShanghaiChina 82 works
  5. 5 New YorkUnited States 75 works
  6. 6 SeoulSouth Korea 55 works
  7. 7 WuhanChina 52 works
  8. 8 TokyoJapan 49 works
  9. 9 HangzhouChina 47 works
  10. 10 GuangzhouChina 46 works

Where it is the local speciality

  1. HanoverDE · 26.3 works5.6×
  2. BethesdaUS · 35.5 works4.7×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Biomedical Text Mining and Ontologies than the world average.

See Biomedical Text Mining and Ontologies on the map

Where is the best place to study Biomedical Text Mining and Ontologies?

Among universities, judged by research, Harvard University, Stanford University and Columbia 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.

0%20%40%mean 21.32%fractional works in this field (log) →share of work in the world top 10% →Stanford UniversityTsinghua UniversityHarvard UniversityColumbia University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Harvard UniversityUnited States 81.319.8%2.8×49 +13.8%
2Stanford UniversityUnited States 68.425.4%3.2×26 -23.1%
3Columbia UniversityUnited States 65.818.9%3.2×19 +23.3%
4Tsinghua UniversityChina 60.822.9%1.6×20 +28.6%
5Radboud University NijmegenNetherlands 60.230.7%2.4×8 +55.6%
6University of Southern CaliforniaUnited States 59.525.3%2.4×11 +45.9%
7University of TorontoCanada 57.618.5%1.6×18 -3.2%
8The University of Texas Health Science Center at HoustonUnited States 56.112.3%12.7×20 +9.8%
9Central South UniversityChina 55.920.3%1.5×16 +91.6%
10Peking UniversityChina 55.719.1%2.0×22 +17.6%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Biomedical Text Mining and Ontologies, country by country

Is Biomedical Text Mining and Ontologies research growing?

Output in 2022-2025 was 15% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Biomedical Text Mining and Ontologies.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Biomedical Text Mining and Ontologies as a percentage of every work published that year, 0.025% in 2024.

still counting0.020%0.040%Biomedical Text Mining and Ontologies0.030% in 202519801990200020102020

Fractional works of Biomedical Text Mining and Ontologies over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Biomedical Text Mining and Ontologies10.0% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

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