World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
30510
World Ranking
5738
National Ranking
2608

Jeffrey T. Leek 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 Jeffrey T. Leek 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: 121 publications — 15th percentile

15% of scientists in this discipline score the same or lower.

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

Jeffrey T. Leek 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 Jeffrey T. Leek 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: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Overview

Jeffrey T. Leek is affiliated with the Fred Hutchinson Cancer Research Center in the United States. Their research spans multiple fields, including Biochemistry, Genetics and Molecular Biology, as well as Computer Science. Key subfields of study include Molecular Biology, Artificial Intelligence, Cancer Research, Information Systems and Management, and Computer Science Applications.

The scientist's work covers several main topics, particularly within Scientific Computing and Data Management, Cancer-related molecular mechanisms research, Genetics, Bioinformatics, and Biomedical Research. Other principal areas include Gene expression and cancer classification, RNA Research and Splicing, RNA modifications and cancer, and Machine Learning in Healthcare.

Frequent co-authors of Jeffrey T. Leek are:

  • Frederick J. Tan
  • Ava M. Hoffman
  • Candace Savonen
  • Tyler H. McCormick
  • Carrie Wright

Publications by Leek have appeared in venues such as arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), Genome Research, F1000Research, and Nature. These venues reflect a focus on genomics, molecular biology, computational biology, and interdisciplinary applications involving computer science.

Selected recent papers include:

  • "Transparency and reproducibility in artificial intelligence," 2020, Nature
  • "recount3: summaries and queries for large-scale RNA-seq expression and splicing," 2021, Genome biology
  • "Functional annotation of human long noncoding RNAs via molecular phenotyping," 2020, Genome Research
  • "Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space," 2022, Cell Genomics
  • "Addressing 6 challenges in generative AI for digital health: A scoping review," 2024, PLOS Digital Health

Best Publications

  • Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

    Mihaela Pertea;Daehwan Kim;Geo M Pertea;Jeffrey T Leek

  • The sva package for removing batch effects and other unwanted variation in high-throughput experiments

    Jeffrey T. Leek;W. Evan Johnson;Hilary S. Parker;Andrew E. Jaffe

  • Tackling the widespread and critical impact of batch effects in high-throughput data

    Jeffrey T. Leek;Robert B. Scharpf;Héctor Corrada Bravo;Héctor Corrada Bravo;David Simcha

  • Capturing heterogeneity in gene expression studies by surrogate variable analysis.

    Jeffrey T Leek;John D Storey

  • Ballgown bridges the gap between transcriptome assembly and expression analysis.

    Alyssa C Frazee;Geo Pertea;Andrew E Jaffe;Ben Langmead

  • Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies.

    Andrew E Jaffe;Peter Murakami;Hwajin Lee;Jeffrey T Leek

  • Temporal dynamics and genetic control of transcription in the human prefrontal cortex

    Carlo Colantuoni;Barbara K. Lipska;Tianzhang Ye;Thomas M. Hyde;Thomas M. Hyde

  • Significance analysis of time course microarray experiments

    John D. Storey;Wenzhong Xiao;Jeffrey T. Leek;Ronald G. Tompkins

  • svaseq: removing batch effects and other unwanted noise from sequencing data

    Jeffrey T. Leek

  • Reproducible RNA-seq analysis using recount2.

    Leonardo Collado-Torres;Abhinav Nellore;Kai Kammers;Kai Kammers;Shannon E Ellis

  • The importance of transparency and reproducibility in artificial intelligence research

    Benjamin Haibe-Kains;George Alexandru Adam;Ahmed Hosny;Farnoosh Khodakarami

  • A general framework for multiple testing dependence

    Jeffrey T. Leek;John D. Storey

  • Cloud-scale RNA-sequencing differential expression analysis with Myrna

    Ben Langmead;Kasper D Hansen;Jeffrey T Leek

  • Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis.

    Andrew E Jaffe;Richard E Straub;Joo Heon Shin;Ran Tao

  • On the design and analysis of gene expression studies in human populations.

    Joshua M Akey;Shameek Biswas;Jeffrey T Leek;John D Storey

  • Polyester: simulating RNA-seq datasets with differential transcript expression

    Alyssa C. Frazee;Andrew E. Jaffe;Ben Langmead;Jeffrey T. Leek

  • EDGE: extraction and analysis of differential gene expression

    Jeffrey T. Leek;Eva Monsen;Alan R. Dabney;John D. Storey

  • recount3: summaries and queries for large-scale RNA-seq expression and splicing

    Christopher Wilks;Shijie C Zheng;Feng Yong Chen;Rone Charles

  • Surrogate variable analysis

    Jeffrey Tullis Leek

  • Statistics: P values are just the tip of the iceberg

    Jeffrey T. Leek;Roger D. Peng

  • Sequencing technology does not eliminate biological variability

    Kasper D Hansen;Zhijin Wu;Rafael A Irizarry;Jeffrey T Leek

  • Opinion: Reproducible research can still be wrong: Adopting a prevention approach

    Jeffrey T. Leek;Roger D. Peng

  • ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets

    Alyssa C Frazee;Ben Langmead;Jeffrey T Leek

  • Transparency and reproducibility in artificial intelligence.

    Benjamin Haibe-Kains;George Alexandru Adam;Ahmed Hosny;Farnoosh Khodakarami;Farnoosh Khodakarami

Frequent Co-Authors

Andrew E. Jaffe
Andrew E. Jaffe Johns Hopkins University
Ben Langmead
Ben Langmead Johns Hopkins University
John D. Storey
John D. Storey Princeton University
Kasper D. Hansen
Kasper D. Hansen Johns Hopkins University
Joel E. Kleinman
Joel E. Kleinman Johns Hopkins University
Rafael A. Irizarry
Rafael A. Irizarry Harvard University
Thomas M. Hyde
Thomas M. Hyde Johns Hopkins University
Benjamin Haibe-Kains
Benjamin Haibe-Kains Princess Margaret Cancer Centre

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