World's Best Scientists 2026 revealed!

D-Index & Metrics

Genetics

D-Index
82
Citations
100391
World Ranking
1434
National Ranking
24

Mark D. Robinson publication distribution in Genetics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Genetics in 2026. The highlighted bar marks where Mark D. Robinson sits on this spectrum.

45–54 publications: 6 scientists 55–64 publications: 10 scientists 65–74 publications: 35 scientists 75–84 publications: 84 scientists 85–94 publications: 102 scientists 95–104 publications: 151 scientists 105–114 publications: 175 scientists 115–124 publications: 203 scientists 125–134 publications: 217 scientists 135–144 publications: 205 scientists 145–154 publications: 193 scientists 155–164 publications: 188 scientists 165–174 publications: 170 scientists 175–184 publications: 178 scientists 185–194 publications: 164 scientists 195–204 publications: 173 scientists 205–214 publications: 159 scientists 215–224 publications: 134 scientists 225–234 publications: 143 scientists 235–244 publications: 105 scientists 245–254 publications: 114 scientists 255–264 publications: 92 scientists 265–274 publications: 88 scientists 275–284 publications: 87 scientists 285–294 publications: 80 scientists 295–304 publications: 62 scientists 305–314 publications: 75 scientists 315–324 publications: 67 scientists 325–334 publications: 60 scientists 335–344 publications: 52 scientists 345–354 publications: 40 scientists 355–364 publications: 48 scientists 365–374 publications: 47 scientists 375–384 publications: 46 scientists 385–394 publications: 31 scientists 395–404 publications: 27 scientists 405–414 publications: 40 scientists 415–424 publications: 30 scientists 425–434 publications: 43 scientists 435–444 publications: 29 scientists 445–454 publications: 14 scientists 455–464 publications: 28 scientists 465–474 publications: 21 scientists 475–484 publications: 21 scientists 485–494 publications: 22 scientists 495–504 publications: 17 scientists 505–514 publications: 12 scientists 515–524 publications: 11 scientists 525–534 publications: 8 scientists 535–544 publications: 8 scientists 545–554 publications: 14 scientists 555–564 publications: 4 scientists 565–574 publications: 11 scientists 575–584 publications: 5 scientists 585–594 publications: 11 scientists 595–604 publications: 12 scientists 605–614 publications: 7 scientists 615–624 publications: 6 scientists 625–634 publications: 10 scientists 635–644 publications: 9 scientists 645–654 publications: 10 scientists 655–664 publications: 6 scientists 665–674 publications: 6 scientists 675–684 publications: 6 scientists 685–694 publications: 4 scientists 695–702 publications: 6 scientists 703+ publications: 100 scientists
45 publications 703+

This scientist: 192 publications — 47th percentile

47% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 703 publications or more.

Mark D. Robinson D-index placement in Genetics in 2026

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

40–41 D-Index: 24 scientists 42–43 D-Index: 52 scientists 44–45 D-Index: 84 scientists 46–47 D-Index: 112 scientists 48–49 D-Index: 118 scientists 50–51 D-Index: 141 scientists 52–53 D-Index: 143 scientists 54–55 D-Index: 145 scientists 56–57 D-Index: 179 scientists 58–59 D-Index: 162 scientists 60–61 D-Index: 175 scientists 62–63 D-Index: 191 scientists 64–65 D-Index: 172 scientists 66–67 D-Index: 184 scientists 68–69 D-Index: 164 scientists 70–71 D-Index: 158 scientists 72–73 D-Index: 150 scientists 74–75 D-Index: 136 scientists 76–77 D-Index: 127 scientists 78–79 D-Index: 127 scientists 80–81 D-Index: 111 scientists 82–83 D-Index: 110 scientists 84–85 D-Index: 110 scientists 86–87 D-Index: 84 scientists 88–89 D-Index: 102 scientists 90–91 D-Index: 66 scientists 92–93 D-Index: 72 scientists 94–95 D-Index: 70 scientists 96–97 D-Index: 54 scientists 98–99 D-Index: 60 scientists 100–101 D-Index: 49 scientists 102–103 D-Index: 55 scientists 104–105 D-Index: 45 scientists 106–107 D-Index: 42 scientists 108–109 D-Index: 28 scientists 110–111 D-Index: 39 scientists 112–113 D-Index: 25 scientists 114–115 D-Index: 31 scientists 116–117 D-Index: 29 scientists 118–119 D-Index: 34 scientists 120–121 D-Index: 29 scientists 122–123 D-Index: 29 scientists 124–125 D-Index: 18 scientists 126–127 D-Index: 27 scientists 128–129 D-Index: 22 scientists 130–131 D-Index: 16 scientists 132–133 D-Index: 11 scientists 134–135 D-Index: 17 scientists 136–137 D-Index: 12 scientists 138–139 D-Index: 21 scientists 140–141 D-Index: 4 scientists 142–143 D-Index: 9 scientists 144–145 D-Index: 14 scientists 146–147 D-Index: 6 scientists 148–149 D-Index: 10 scientists 150–151 D-Index: 7 scientists 152–153 D-Index: 9 scientists 154–155 D-Index: 8 scientists 156–157 D-Index: 8 scientists 158–159 D-Index: 9 scientists 160+ D-Index: 96 scientists
40 D-Index 160+

This scientist: 82 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 160 D-Index or more.

Overview

Mark D. Robinson is affiliated with the University of Zurich in Switzerland. Their research work primarily focuses on the field of Biochemistry, Genetics and Molecular Biology, with 226 publications contributing to this domain.

Their subfields of study include Molecular Biology, Immunology, Cancer Research, Oncology, and Biophysics. These areas highlight a broad engagement with cellular and molecular processes, disease mechanisms, and physical principles underlying biological function.

Key topics within their work include:

  • Single-cell and spatial transcriptomics
  • Gene expression and cancer classification
  • RNA Research and Splicing
  • Cancer Genomics and Diagnostics
  • Cell Image Analysis Techniques
  • Genomics and Phylogenetic Studies
  • Epigenetics and DNA Methylation

Mark D. Robinson has contributed extensively to high-profile journals and preprint platforms, frequently publishing in:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Zenodo (CERN European Organization for Nuclear Research)
  • F1000Research
  • Genome biology
  • The Journal of Immunology

Recent significant papers authored by or involving Mark D. Robinson include:

  • Eleven grand challenges in single-cell data science, 2020, Genome biology
  • Doublet identification in single-cell sequencing data using scDblFinder, 2021, F1000Research
  • Doublet identification in single-cell sequencing data using scDblFinder, 2022, F1000Research
  • muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data, 2020, Nature Communications
  • A systematic performance evaluation of clustering methods for single-cell RNA-seq data, 2020, F1000Research

Collaborations feature prominently in their work, with frequent co-authors including:

  • Charlotte Soneson
  • Helena L. Crowell
  • Pierre-Luc Germain
  • Silvia Guglietta
  • Lukas M. Weber

Best Publications

  • edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

    Mark D. Robinson;Davis J. McCarthy;Gordon K. Smyth

  • A scaling normalization method for differential expression analysis of RNA-seq data

    Mark D Robinson;Mark D Robinson;Alicia Oshlack

  • Comprehensive genomic characterization defines human glioblastoma genes and core pathways

    Roger McLendon;Allan Friedman;Darrell Bigner;Erwin G. Van Meir

  • Global landscape of protein complexes in the yeast Saccharomyces cerevisiae

    Nevan J. Krogan;Gerard Cagney;Gerard Cagney;Haiyuan Yu;Gouqing Zhong

  • Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

    Charlotte Soneson;Charlotte Soneson;Michael I. Love;Mark D. Robinson;Mark D. Robinson

  • Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants

    Amy Hin Yan Tong;Marie Evangelista;Ainslie B. Parsons;Hong Xu

  • Count-based differential expression analysis of RNA sequencing data using R and Bioconductor

    Simon Anders;Davis J McCarthy;Davis J McCarthy;Yunshun Chen;Yunshun Chen;Michal Okoniewski

  • Eleven grand challenges in single-cell data science

    David Lähnemann;David Lähnemann;Johannes Köster;Johannes Köster;Ewa Szczurek;Davis J. McCarthy;Davis J. McCarthy

  • Small-sample estimation of negative binomial dispersion, with applications to SAGE data

    Mark D. Robinson;Gordon K. Smyth

  • Large‐scale mapping of human protein–protein interactions by mass spectrometry

    Rob M. Ewing;Peter Chu;Fred Elisma;Hongyan Li

  • Moderated statistical tests for assessing differences in tag abundance

    Mark D. Robinson;Gordon K. Smyth

  • From RNA-seq reads to differential expression results.

    Alicia Oshlack;Mark D Robinson;Mark D Robinson;Matthew D Young

  • High-throughput mapping of a dynamic signaling network in mammalian cells.

    Miriam Barrios-Rodiles;Kevin R. Brown;Barish Ozdamar;Barish Ozdamar;Rohit Bose;Rohit Bose

  • High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy

    Carsten Krieg;Malgorzata Nowicka;Malgorzata Nowicka;Silvia Guglietta;Sabrina Schindler

  • Bias, robustness and scalability in single-cell differential expression analysis

    Charlotte Soneson;Charlotte Soneson;Mark D Robinson;Mark D Robinson

  • ESHRE PGD Consortium ‘Best practice guidelines for clinical preimplantation genetic diagnosis (PGD) and preimplantation genetic screening (PGS)’

    A.R. Thornhill;C.E. deDie-Smulders;J.P. Geraedts;J.C. Harper

  • FunSpec: a web-based cluster interpreter for yeast

    Mark D Robinson;Jörg Grigull;Naveed Mohammad;Timothy R Hughes

  • High-Definition Macromolecular Composition of Yeast RNA-Processing Complexes

    Nevan J. Krogan;Wen-Tao Peng;Gerard Cagney;Mark D. Robinson

  • Robustly detecting differential expression in RNA sequencing data using observation weights

    Xiaobei Zhou;Xiaobei Zhou;Helen Lindsay;Helen Lindsay;Mark D. Robinson;Mark D. Robinson

  • Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters

    Lani F. Wu;Timothy R. Hughes;Armaity P. Davierwala;Mark D. Robinson

Frequent Co-Authors

Susan J. Clark
Susan J. Clark Garvan Institute of Medical Research
José Iriarte
José Iriarte University of Exeter
Clare Stirzaker
Clare Stirzaker Garvan Institute of Medical Research
Terence P. Speed
Terence P. Speed Walter and Eliza Hall Institute of Medical Research
Gordon K. Smyth
Gordon K. Smyth Walter and Eliza Hall Institute of Medical Research
Burkhard Becher
Burkhard Becher University of Zurich
Christian von Mering
Christian von Mering University of Zurich
Quaid Morris
Quaid Morris Memorial Sloan Kettering Cancer Center
John C. Marioni
John C. Marioni European Bioinformatics Institute
Charles Boone
Charles Boone University of Toronto

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