Question 1

Which countries lead Biomedical Text Mining and Ontologies research?

Accepted Answer

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

Question 2

Which institutions lead Biomedical Text Mining and Ontologies research?

Accepted Answer

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.

Question 3

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

Accepted Answer

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

Question 4

Where is Biomedical Text Mining and Ontologies research done?

Accepted Answer

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.

Question 5

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

Accepted Answer

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.

Question 6

Is Biomedical Text Mining and Ontologies research growing?

Accepted Answer

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

Question 7

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

Accepted Answer

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).