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Topic · Oncology

Peptidase Inhibition and Analysis

Peptidase Inhibition and Analysis is a research topic within Oncology. Science Explorer counts 57k research works in it since 1950. 18.9% of them reached the world's top 10% most cited for their field and year.

This cluster of papers explores the role of fibroblast activation in cancer progression, focusing on the expression of fibroblast activation protein, immunosuppression in the tumor microenvironment, N-terminal acetylation of proteins, protease inhibitors for cancer therapy, angiogenesis, and PET imaging to target stromal cells in the tumor microenvironment.

  • Fibroblast Activation Protein
  • Tumor Microenvironment
  • Immunosuppression
  • Cancer Therapy
  • N-terminal Acetylation
  • Protease Inhibitors
  • Angiogenesis
  • PET Imaging
  • Stromal Cells
  • Tumor Stroma
Research works
57k
fractional, since 1950
In the world top 10%
11k
per year above
Top-10% rate
18.9%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+17%
the tick is no change

This ranks research output and citation impact. It says nothing about the quality of diagnosis, treatment or care.

Which countries lead Peptidase Inhibition and Analysis research?

By volume, China and the United States publish the most (2.9k and 1.9k works in 2022–2025).

By volume, 2022–2025

  1. 1 China 2.9k works
  2. 2 United States 1.9k works
  3. 3 Germany 471 works
  4. 4 India 450 works
  5. 5 Japan 412 works
  6. 6 United Kingdom 301 works
  7. 7 Italy 288 works
  8. 8 France 217 works
  9. 9 South Korea 204 works
  10. 10 Spain 191 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.

China: 39.1%United States: 26.3%Germany: 6.4%India: 6.1%6 others listed: 22.0%39%largest
China2,867 · 39.1%United States1,923 · 26.3%Germany471 · 6.4%India450 · 6.1%6 others listed1,613 · 22.0%

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

Which institutions lead Peptidase Inhibition and Analysis research?

By volume in 2022–2025, Chinese Academy of Medical Sciences & Peking Union Medical College publishes the most Peptidase Inhibition and Analysis research, followed by Sun Yat-sen University and Sichuan University.

Who are the leading researchers in Peptidase Inhibition and Analysis?

The most-cited researchers publishing on Peptidase Inhibition and Analysis include Dirk Schadendorf.

  1. 1 Dirk Schadendorf Germany 4.4k citations

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

Where is Peptidase Inhibition and Analysis research done?

The largest centres of Peptidase Inhibition and Analysis research in 2022–2025 are Beijing (China), Shanghai (China), Guangzhou (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Luzhou and Heidelberg.

Largest cities, 2022–2025

  1. 1 Beijing China 375 works
  2. 2 Shanghai China 286 works
  3. 3 Guangzhou China 219 works
  4. 4 Nanjing China 131 works
  5. 5 Hangzhou China 119 works
  6. 6 Wuhan China 116 works
  7. 7 Tokyo Japan 109 works
  8. 8 Seoul South Korea 106 works
  9. 9 New York United States 106 works
  10. 10 Chengdu China 104 works

Where it is the local speciality

  1. LuzhouCN · 51.8 works24×
  2. HeidelbergDE · 51.8 works5.7×
← less than its size predictsmore →

Location quotient: how much more of its research is in Peptidase Inhibition and Analysis than the world average.

See Peptidase Inhibition and Analysis on the map

Where is the best place to study Peptidase Inhibition and Analysis?

Among universities, judged by research, China Pharmaceutical University, Chinese Academy of Medical Sciences & Peking Union Medical College and Shenyang Pharmaceutical 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 27.82%fractional works in this node (log) →share in the world top 10% →China Pharmaceutical University: 26, 40.1%Chinese Academy of Medical Sciences & Peking Union Medical College: 65, 23.8%Shenyang Pharmaceutical University: 11, 23.7%Mashhad University of Medical Sciences: 18, 20.2%Southern Medical University: 22, 36.3%Guangzhou Medical University: 18, 28.0%Second Military Medical University: 15, 25.0%Heidelberg University: 21, 28.9%Shandong First Medical University: 20, 22.5%Zhengzhou University: 21, 29.7%China Pharmaceutical…Chinese Academy of M…Shenyang Pharmaceuti…Mashhad University o…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 China Pharmaceutical UniversityChina 68.740.1%13.9×26 +14.0%
2 Chinese Academy of Medical Sciences & Peking Union Medical CollegeChina 59.023.8%7.6×65 -0.0%
3 Shenyang Pharmaceutical UniversityChina 58.523.7%9.7×11 +330.9%
4 Mashhad University of Medical SciencesIran 57.220.2%8.7×18 +160.8%
5 Southern Medical UniversityChina 56.036.3%7.0×22 -1.3%
6 Guangzhou Medical UniversityChina 53.828.0%7.5×18 +71.8%
7 Second Military Medical UniversityChina 50.825.0%14.7×15 -13.8%
8 Heidelberg UniversityGermany 50.128.9%4.6×21 +28.8%
9 Shandong First Medical UniversityChina 49.422.5%9.0×20 -8.6%
10 Zhengzhou UniversityChina 49.029.7%2.8×21 +159.2%

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 Peptidase Inhibition and Analysis research growing?

Output in 2018–2022 was 17% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Peptidase Inhibition and Analysis.

19801990200020102020
grewheldshrank

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.