World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
7655
World Ranking
12903
National Ranking
116

Juho Rousu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Juho Rousu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 150 publications — 27th percentile

27% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Juho Rousu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Juho Rousu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Juho Rousu is affiliated with Aalto University in Finland. Their research integrates multiple disciplines, focusing largely on biochemistry, genetics, molecular biology, and computer science. They have published extensively on topics related to computational drug discovery, machine learning in materials science, and metabolomics studies.

Their main fields of study include:

  • Biochemistry, Genetics and Molecular Biology
  • Computer Science

Rousu's subfields of study encompass:

  • Molecular Biology
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Materials Chemistry
  • Spectroscopy

Their research topics cover:

  • Computational Drug Discovery Methods
  • Machine Learning in Materials Science
  • Metabolomics and Mass Spectrometry Studies
  • Bioinformatics and Genomic Networks
  • Analytical Chemistry and Chromatography
  • Mass Spectrometry Techniques and Applications
  • Microbial Natural Products and Biosynthesis

Frequent coauthors include:

  • Sándor Szedmák (18 publications)
  • Tero Aittokallio (14 publications)
  • Tianduanyi Wang (11 publications)
  • Anna Cichońska (8 publications)
  • Tapio Pahikkala (8 publications)

Rousu has published papers in several prominent venues. The most frequent publication venues are:

  • bioRxiv (Cold Spring Harbor Laboratory) - 11 publications
  • arXiv (Cornell University) - 9 publications
  • Bioinformatics - 6 publications
  • Nature Communications - 2 publications
  • PLoS Computational Biology - 2 publications

Recent publications include the following:

  • Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra, 2020, Nature Biotechnology
  • Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects, 2020, Nature Communications
  • Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions, 2021, PLoS Computational Biology
  • Systematic review of computational methods for drug combination prediction, 2022, Computational and Structural Biotechnology Journal
  • Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods, 2020, Applied Microbiology and Biotechnology

Best Publications

  • SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information.

    Kai Dührkop;Markus Fleischauer;Marcus Ludwig;Alexander A. Aksenov

  • Searching molecular structure databases with tandem mass spectra using CSI:FingerID.

    Kai Dührkop;Huibin Shen;Marvin Meusel;Juho Rousu

  • Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra

    Kai Dührkop;Louis Felix Nothias;Markus Fleischauer;Raphael Reher

  • Kernel-Based Learning of Hierarchical Multilabel Classification Models

    Juho Rousu;Craig Saunders;Sandor Szedmak;John Shawe-Taylor

  • General and Efficient Multisplitting of Numerical Attributes

    Tapio Elomaa;Juho Rousu

  • Metabolite identification and molecular fingerprint prediction through machine learning

    Markus Heinonen;Huibin Shen;Nicola Zamboni;Juho Rousu

  • Critical Assessment of Small Molecule Identification 2016: automated methods

    Emma L. Schymanski;Christoph Ruttkies;Martin Krauss;Céline Brouard;Céline Brouard

  • FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data.

    Markus Heinonen;Ari Rantanen;Ari Rantanen;Taneli Mielikäinen;Juha Kokkonen

  • metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

    Anna Cichonska;Juho Rousu;Pekka Marttinen;Antti J. Kangas

  • Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects.

    Heli Julkunen;Anna Cichonska;Anna Cichonska;Anna Cichonska;Prson Gautam;Sandor Szedmak

  • Metabolite identification through multiple kernel learning on fragmentation trees.

    Huibin Shen;Kai Dührkop;Sebastian Böcker;Juho Rousu

  • Learning hierarchical multi-category text classification models

    Juho Rousu;Craig Saunders;Sandor Szedmak;John Shawe-Taylor

  • Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors

    Anna Cichonska;Balaguru Ravikumar;Elina Parri;Sanna Timonen

  • Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species

    Esa Pitkänen;Paula Jouhten;Jian Hou;Muhammad Fahad Syed

  • Fast metabolite identification with Input Output Kernel Regression

    Céline Brouard;Huibin Shen;Kai Dührkop;Florence d'Alché-Buc

  • Learning with multiple pairwise kernels for drug bioactivity prediction

    Anna Cichonska;Anna Cichonska;Tapio Pahikkala;Sandor Szedmak;Heli Julkunen

  • Inferring branching pathways in genome-scale metabolic networks

    Esa Pitkänen;Paula Jouhten;Juho Rousu

  • A Tutorial on Canonical Correlation Methods

    Viivi Uurtio;João M. Monteiro;Jaz Kandola;John Shawe-Taylor

  • Efficient Multisplitting Revisited: Optima-Preserving Elimination of Partition Candidates

    Tapio Elomaa;Juho Rousu

  • Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions.

    Grímur Hjörleifsson Eldjárn;Andrew Ramsay;Justin J J van der Hooft;Katherine R Duncan

  • Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

    Markus Heinonen;Henrik Mannerström;Juho Rousu;Samuel Kaski

  • Liquid-chromatography retention order prediction for metabolite identification.

    Eric Bach;Sandor Szedmak;Céline Brouard;Sebastian Böcker

Frequent Co-Authors

Esko Ukkonen
Esko Ukkonen University of Helsinki
Samuel Kaski
Samuel Kaski Aalto University
John Shawe-Taylor
John Shawe-Taylor University College London
Sebastian Böcker
Sebastian Böcker Friedrich Schiller University Jena
Tero Aittokallio
Tero Aittokallio University of Helsinki
Harri Lähdesmäki
Harri Lähdesmäki Aalto University
Tapio Pahikkala
Tapio Pahikkala University of Turku
Liisa Holm
Liisa Holm University of Helsinki
Samuli Ripatti
Samuli Ripatti University of Helsinki
Nicola Zamboni
Nicola Zamboni ETH Zurich

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

If you’re exploring a future in Computer Science, there’s a wide range of online degrees and certifications that can help you enter the field or advance your career. For those seeking a quick transition, short certificate programs that pay well offer focused, practical training in areas like cybersecurity, data analytics, or IT support. These programs are ideal for learners looking to upskill without a long-term commitment.

Online degrees are also available at various levels. For example, 2 year online degrees provide a foundation in programming and technology, making them a good option for entry-level roles or as a stepping stone to further studies.

For quicker advancement, look into the shortest masters degree programs online. These accelerated programs allow students to obtain an advanced qualification in as little as one year, improving job prospects and potential earning power. Choosing from the most useful masters degrees ensures that your education aligns closely with in-demand skills and industry trends, such as artificial intelligence, machine learning, or information security.

Best Scientists Citing Juho Rousu

Trending Scientists

Recently Published Articles