World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
5593
World Ranking
9409
National Ranking
585

Magnus Rattray 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 Magnus Rattray 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: 142 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Magnus Rattray 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 Magnus Rattray 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: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

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

Overview

Magnus Rattray is affiliated with the University of Manchester in the United Kingdom. Their research primarily focuses on Biochemistry, Genetics, and Molecular Biology, with 99 publications in this main field of study.

The scientist has worked extensively within several subfields, including Molecular Biology, Immunology, Genetics, Infectious Diseases, and Epidemiology. Their key research topics span:

  • Single-cell and spatial transcriptomics
  • Genomics and Chromatin Dynamics
  • RNA Research and Splicing
  • Gene Regulatory Network Analysis
  • T-cell and B-cell Immunology
  • COVID-19 Clinical Research Studies
  • RNA modifications and cancer

Among the recent papers authored or co-authored by Magnus Rattray are:

  • "Longitudinal immune profiling reveals key myeloid signatures associated with COVID-19" (2020, Science Immunology)
  • "Modulation of the Promoter Activation Rate Dictates the Transcriptional Response to Graded BMP Signaling Levels in the Drosophila Embryo" (2020, Developmental Cell)
  • "Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis" (2020, Nature Communications)
  • "Murine AGM single-cell profiling identifies a continuum of hemogenic endothelium differentiation marked by ACE" (2021, Blood)
  • "Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments" (2021, Bioinformatics)

Magnus Rattray frequently publishes in several notable venues, including:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Bioinformatics
  • Scientific Reports
  • Nucleic Acids Research
  • Lara D. Veeken

The scientist collaborates regularly with several co-authors, among whom are:

  • Hilary L. Ashe
  • Stephen Eyre
  • Angela Simpson
  • Antony Adamson
  • Paul Martin

Best Publications

  • Making sense of big data in health research: Towards an EU action plan

    Charles Auffray;Charles Auffray;Rudi Balling;Inês Barroso;László Bencze

  • Identifying differentially expressed transcripts from RNA-seq data with biological variation

    Peter Glaus;Antti Honkela;Magnus Rattray

  • Gene expression profiling in human neurodegenerative disease

    Johnathan Cooper-Knock;Janine Kirby;Laura Ferraiuolo;Paul R. Heath

  • Bayesian Phylogenetics Using an RNA Substitution Model Applied to Early Mammalian Evolution

    H. Jow;C. Hudelot;M. Rattray;P. G. Higgs

  • Making sense of microarray data distributions.

    David C. Hoyle;Magnus Rattray;Ray Jupp;Andrew Brass

  • Gaussian process modelling of latent chemical species

    Pei Gao;Antti Honkela;Magnus Rattray;Neil D. Lawrence

  • Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities

    Guido Sanguinetti;Neil D. Lawrence;Magnus Rattray

  • Modelling transcriptional regulation using Gaussian Processes

    Neil D. Lawrence;Guido Sanguinetti;Magnus Rattray

  • Model-based Method for Transcription Factor Target Identification with Limited Data

    Antti Honkela;Charles Girardot;E. Hilary Gustafson;Ya Hsin Liu

  • Distinguishing Asthma Phenotypes Using Machine Learning Approaches

    Rebecca Howard;Magnus Rattray;Mattia Prosperi;Mattia Prosperi;Adnan Custovic;Adnan Custovic

  • A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips

    Xuejun Liu;Marta Milo;Neil D. Lawrence;Magnus Rattray

  • Fast Variational Inference in the Conjugate Exponential Family

    James Hensman;Magnus Rattray;Neil D. Lawrence

  • Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters

    James Hensman;Neil D Lawrence;Magnus Rattray

  • Natural gradient descent for on-line learning

    Magnus Rattray;David Saad;Shun-ichi Amari

  • A tumor progression model for hepatocellular carcinoma: bioinformatic analysis of genomic data.

    Terence C.W. Poon;Terence C.W. Poon;Nathalie Wong;Paul B.S. Lai;Magnus Rattray

  • Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays

    Antti Honkela;Jaakko Peltonen;Hande Topa;Iryna Charapitsa

  • Probe-level measurement error improves accuracy in detecting differential gene expression

    Xuejun Liu;Marta Milo;Neil D Lawrence;Magnus Rattray

  • puma: a Bioconductor package for propagating uncertainty in microarray analysis

    Richard D Pearson;Richard D Pearson;Xuejun Liu;Guido Sanguinetti;Marta Milo

  • Evolutionary Systems Biology of Amino Acid Biosynthetic Cost in Yeast

    Michael D. Barton;Michael D. Barton;Daniela Delneri;Stephen G. Oliver;Stephen G. Oliver;Magnus Rattray

  • Accounting for probe-level noise in principal component analysis of microarray data

    Guido Sanguinetti;Marta Milo;Magnus Rattray;Neil D. Lawrence

  • Principal-component-analysis eigenvalue spectra from data with symmetry-breaking structure

    D. C. Hoyle;M. Rattray

  • Genome-wide occupancy links Hoxa2 to Wnt–β-catenin signaling in mouse embryonic development

    Ian J. Donaldson;Shilu Amin;James J. Hensman;Eva Kutejova

Frequent Co-Authors

Neil D. Lawrence
Neil D. Lawrence University of Cambridge
David Saad
David Saad Aston University
Stephen G. Oliver
Stephen G. Oliver University of Cambridge
Guido Sanguinetti
Guido Sanguinetti International School for Advanced Studies
David W. Ray
David W. Ray University of Oxford
Nancy Papalopulu
Nancy Papalopulu University of Manchester
Christoph Bock
Christoph Bock Austrian Academy of Sciences
Xuejun Liu
Xuejun Liu China Agricultural University
Angela Simpson
Angela Simpson University of Manchester
David G. Spiller
David G. Spiller University of Manchester

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