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Topic · Molecular Biology

Machine Learning in Bioinformatics

Machine Learning in Bioinformatics is a research topic within Molecular Biology. Science Explorer counts 26k research works in it since 1950. 9.9% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the prediction of protein subcellular localization using various computational methods such as amino acid composition, machine learning algorithms like support vector machines, and the analysis of signal peptides and transmembrane topology. The research aims to improve the accuracy and reliability of predicting the subcellular location of proteins, which has significant implications for understanding protein function and cellular processes.

  • Subcellular Localization
  • Protein
  • Prediction
  • Amino Acid Composition
  • Machine Learning
  • Support Vector Machines
  • Signal Peptides
  • Transmembrane Topology
  • Enzyme Subfamily Classes
  • Bioinformatics
Research works
26k
fractional, since 1950
In the world top 10%
2.6k
per year above
Top-10% rate
9.9%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+30%
the tick is no change
Largest producer
China
43% of the 10 largest, 2022-now
Machine Learning in Bioinformatics

Which countries lead Machine Learning in Bioinformatics research?

By volume, China and the United States publish the most (2k and 1.1k works in 2022-now).

By volume, 2022-now

  1. 1 China 2k works
  2. 2 United States 1.1k works
  3. 3 India 760 works
  4. 4 Germany 167 works
  5. 5 United Kingdom 167 works
  6. 6 Indonesia 154 works
  7. 7 South Korea 123 works
  8. 8 Japan 122 works
  9. 9 Canada 112 works
  10. 10 France 103 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: 42.5%United States: 21.9%India: 15.8%Germany: 3.5%6 others listed: 16.3%43%largest
China2,041 · 42.5%United States1,052 · 21.9%India760 · 15.8%Germany167 · 3.5%6 others listed781 · 16.3%

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

Which institutions lead Machine Learning in Bioinformatics research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Machine Learning in Bioinformatics research, followed by Centre National de la Recherche Scientifique and Central South University.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 64 works
  2. 2 Centre National de la Recherche ScientifiqueFrance 36 works
  3. 3 Central South UniversityChina 35 works
  4. 4 Shanghai Jiao Tong UniversityChina 33 works
  5. 5 University of Electronic Science and Technology of ChinaChina 28 works
  6. 6 Harvard UniversityUnited States 27 works
  7. 7 Russian Academy of SciencesRussia 26 works
  8. 8 Shandong UniversityChina 26 works
  9. 9 Zhejiang UniversityChina 26 works
  10. 10 Tsinghua UniversityChina 24 works

Who are the leading researchers in Machine Learning in Bioinformatics?

The most-cited researchers publishing on Machine Learning in Bioinformatics include Eric S. Lander, Yoshua Bengio and Rob Knight.

  1. 1 Eric S. Lander United States 524k citations
  2. 2 Yoshua Bengio Canada 476k citations
  3. 3 Rob Knight United States 468k citations
  4. 4 George Davey Smith United Kingdom 435k citations
  5. 5 Richard M. Durbin United Kingdom 410k citations
  6. 6 Mark J. Daly United States 403k citations
  7. 7 Peer Bork Germany 396k citations
  8. 8 Robert John Tibshirani United States 396k citations
  9. 9 Steven L. Salzberg United States 372k citations
  10. 10 Gad A. Getz United States 364k 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 Machine Learning in Bioinformatics?

The most cited paper in Machine Learning in Bioinformatics is "CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice" (Julie Thompson, Desmond G. Higgins, Toby J. Gibson, 1994), with 65,356 citations. Next come "Highly accurate protein structure prediction with AlphaFold" (47,486) and "clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters" (39,704).

Most cited papers
#PaperReleasedCitations
1 CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice Julie Thompson, Desmond G. Higgins, Toby J. Gibson · Nucleic Acids Research Open access 1994 65,356
2 Highly accurate protein structure prediction with AlphaFold John Jumper et al. · Nature Open access 15 Jul 2021 47,486
3 clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters Guangchuang Yu, Li-Gen Wang, Yanyan Han et al. · OMICS A Journal of Integrative Biology Open access 28 Mar 2012 39,704
4 Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources Da Wei Huang, Brad T. Sherman, Richard A. Lempicki · Nature Protocols 18 Dec 2008 37,929
5 Search and clustering orders of magnitude faster than BLAST R. C. Edgar · Bioinformatics Open access 12 Aug 2010 22,013
All 100 most cited papers, with filters and sorting

Where is Machine Learning in Bioinformatics research done?

The largest centres of Machine Learning in Bioinformatics research in 2022-now are Beijing (China), Shanghai (China), Changsha (China) and Chennai (India).

Largest cities, 2022-now

  1. 1 BeijingChina 261 works
  2. 2 ShanghaiChina 148 works
  3. 3 ChangshaChina 88 works
  4. 4 ChennaiIndia 78 works
  5. 5 WuhanChina 78 works
  6. 6 HangzhouChina 74 works
  7. 7 ShenzhenChina 73 works
  8. 8 Xi'anChina 72 works
  9. 9 NanjingChina 71 works
  10. 10 ChengduChina 68 works
See Machine Learning in Bioinformatics on the map

Where is the best place to study Machine Learning in Bioinformatics?

Among universities, judged by research, Harvard University, University of Electronic Science and Technology of China and Taipei Medical 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 20.38%fractional works in this field (log) →share of work in the world top 10% →Taipei Medical Unive…University of Electr…Harvard UniversityCentral South Univer…
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 74.622.5%1.5×27 +36.8%
2University of Electronic Science and Technology of ChinaChina 71.323.3%4.5×28 -29.2%
3Taipei Medical UniversityTaiwan 65.825.0%8.1×9 +85.7%
4Central South UniversityChina 65.112.1%3.3×35 +63.4%
5Shandong UniversityChina 64.720.8%3.5×26 +38.3%
6Shanghai Jiao Tong UniversityChina 63.410.4%2.1×33 +39.1%
7Sungkyunkwan UniversitySouth Korea 63.230.4%2.8×11 +621.9%
8Massachusetts Institute of TechnologyUnited States 62.422.4%3.5×14 +146.9%
9University College LondonUnited Kingdom 62.120.9%1.5×14 +42.2%
10Stanford UniversityUnited States 60.016.0%2.1×17 +56.9%

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 Machine Learning in Bioinformatics, country by country

Is Machine Learning in Bioinformatics research growing?

Output in 2022-2025 was 30% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Machine Learning in Bioinformatics.

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 Machine Learning in Bioinformatics as a percentage of every work published that year, 0.024% in 2024.

still counting0.020%0.040%Machine Learning in Bioinformatics0.029% in 202519801990200020102020

Fractional works of Machine Learning in Bioinformatics 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%Machine Learning in Bioinformatics9.9% 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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