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Topic · Spectroscopy

Advanced Proteomics Techniques and Applications

Advanced Proteomics Techniques and Applications is a research topic within Spectroscopy. Science Explorer counts 27k research works in it since 1950. 24.3% of them reached the world's top 10% most cited for their field and year.

This cluster of papers covers advancements in mass spectrometry techniques, with a focus on proteomics, quantitative analysis, protein identification, phosphoproteomics, and biomarker discovery. It includes topics such as label-free quantification, tandem mass spectrometry, protein phosphorylation, and data-independent acquisition.

  • Proteomics
  • Mass Spectrometry
  • Quantitative Analysis
  • Protein Identification
  • Phosphoproteomics
  • Biomarker Discovery
  • Label-free Quantification
  • Tandem Mass Spectrometry
  • Protein Phosphorylation
  • Data-independent Acquisition
Research works
27k
fractional, since 1950
In the world top 10%
6.5k
per year above
Top-10% rate
24.3%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
-7%
the tick is no change

Which countries lead Advanced Proteomics Techniques and Applications research?

By volume, the United States and China publish the most (977 and 642 works in 2022–2025).

By volume, 2022–2025

  1. 1 United States 977 works
  2. 2 China 642 works
  3. 3 Germany 247 works
  4. 4 United Kingdom 155 works
  5. 5 India 133 works
  6. 6 Canada 115 works
  7. 7 France 106 works
  8. 8 Japan 98 works
  9. 9 Spain 70 works
  10. 10 Netherlands 70 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: 37.4%China: 24.6%Germany: 9.5%United Kingdom: 5.9%6 others listed: 22.7%37%largest
United States977 · 37.4%China642 · 24.6%Germany247 · 9.5%United Kingdom155 · 5.9%6 others listed592 · 22.7%

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

Which institutions lead Advanced Proteomics Techniques and Applications research?

By volume in 2022–2025, University of Washington publishes the most Advanced Proteomics Techniques and Applications research, followed by University of Wisconsin–Madison and Chinese Academy of Sciences.

Who are the leading researchers in Advanced Proteomics Techniques and Applications?

The most-cited researchers publishing on Advanced Proteomics Techniques and Applications include Vamsi K. Mootha, Amanda G. Paulovich and Matthias Mann.

  1. 1 Vamsi K. Mootha United States 6.9k citations
  2. 2 Amanda G. Paulovich United States 5.5k citations
  3. 3 Matthias Mann Germany 5.1k citations

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

Where is Advanced Proteomics Techniques and Applications research done?

The largest centres of Advanced Proteomics Techniques and Applications research in 2022–2025 are Beijing (China), Shanghai (China), Paris (France) and Moscow (Russia). Among places with at least 20 works in it, it is an unusually large share of all research in Madison, Seattle and Copenhagen.

Largest cities, 2022–2025

  1. 1 Beijing China 149 works
  2. 2 Shanghai China 80 works
  3. 3 Paris France 48 works
  4. 4 Moscow Russia 43 works
  5. 5 London United Kingdom 41 works
  6. 6 Seattle United States 39 works
  7. 7 Boston United States 35 works
  8. 8 Hangzhou China 35 works
  9. 9 Guangzhou China 33 works
  10. 10 Copenhagen Denmark 33 works

Where it is the local speciality

  1. MadisonUS · 28.2 works7.0×
  2. SeattleUS · 39.4 works6.2×
  3. CopenhagenDK · 33.1 works5.8×
← less than its size predictsmore →

Location quotient: how much more of its research is in Advanced Proteomics Techniques and Applications than the world average.

See Advanced Proteomics Techniques and Applications on the map

Where is the best place to study Advanced Proteomics Techniques and Applications?

Among universities, judged by research, University of Copenhagen, University of Washington and University of Wisconsin–Madison 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%60%mean 34.63%fractional works in this node (log) →share in the world top 10% →University of Copenhagen: 15, 32.2%University of Washington: 25, 25.4%University of Wisconsin–Madison: 23, 26.6%Northeastern University: 9, 39.5%Michigan State University: 10, 53.6%University of Oklahoma: 8, 35.4%Technical University of Munich: 13, 37.0%Karolinska Institutet: 9, 35.3%Harvard University: 15, 32.1%Baylor College of Medicine: 9, 29.2%Northeastern Univers…University of Copenh…University of Wiscon…University of Washin…
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 University of CopenhagenDenmark 55.232.2%6.8×15 +20.1%
2 University of WashingtonUnited States 54.825.4%7.3×25 -19.9%
3 University of Wisconsin–MadisonUnited States 54.026.6%7.5×23 -17.9%
4 Northeastern UniversityUnited States 53.839.5%8.9×9 +24.9%
5 Michigan State UniversityUnited States 51.353.6%4.1×10 +72.8%
6 University of OklahomaUnited States 51.335.4%9.6×8
7 Technical University of MunichGermany 46.837.0%4.8×13 +8.9%
8 Karolinska InstitutetSweden 45.235.3%6.1×9 -17.9%
9 Harvard UniversityUnited States 43.532.1%3.7×15 +0.5%
10 Baylor College of MedicineUnited States 39.829.2%6.6×9 +56.5%

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 Advanced Proteomics Techniques and Applications research growing?

Output in 2018–2022 was 7% lower than in 2013–2017, peaking in 2012. The fastest-growing topics are Advanced Proteomics Techniques and Applications.

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.