World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
51
Citations
16456
World Ranking
3757
National Ranking
1096

Casey S. Greene publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Casey S. Greene sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 195 publications — 46th percentile

46% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Casey S. Greene D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Casey S. Greene sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 51 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Casey S. Greene is affiliated with the University of Colorado Denver in the United States. Their research primarily spans the fields of Biochemistry, Genetics and Molecular Biology, and Medicine, with numerous publications contributing to these domains.

Their work extensively covers subfields such as Molecular Biology, Cancer Research, Genetics, Artificial Intelligence, and Information Systems and Management. This multidisciplinary approach is reflected in the breadth of topics they address, which include:

  • Gene expression and cancer classification
  • Bioinformatics and Genomic Networks
  • Cancer Genomics and Diagnostics
  • Single-cell and spatial transcriptomics
  • Cancer-related molecular mechanisms research
  • Molecular Biology Techniques and Applications
  • Cell Image Analysis Techniques

Casey S. Greene has contributed to several recent papers, such as:

  • Transparency and reproducibility in artificial intelligence (2020), published in Nature
  • Constructing knowledge graphs and their biomedical applications (2020), published in Computational and Structural Biotechnology Journal
  • Characterizing Long COVID: Deep Phenotype of a Complex Condition (2021), published in EBioMedicine
  • Reproducibility standards for machine learning in the life sciences (2021), published in Nature Methods
  • Prognostic gene expression signature for high-grade serous ovarian cancer (2020), published in Annals of Oncology

Their collaboration network includes frequent coauthors such as Alexandra Lee, Jake Crawford, Jennifer A. Doherty, Natalie R. Davidson, and Jaclyn Taroni. These collaborations appear across diverse topics within their research scope.

The venues where Casey S. Greene often publishes include:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Zenodo (CERN European Organization for Nuclear Research)
  • arXiv (Cornell University)
  • Cancer Research
  • Genome Biology

Best Publications

  • Oncogenic Signaling Pathways in The Cancer Genome Atlas

    Francisco Sanchez-Vega;Marco Mina;Joshua Armenia;Walid K. Chatila

  • Opportunities and obstacles for deep learning in biology and medicine.

    Travers Ching;Daniel S. Himmelstein;Brett K. Beaulieu-Jones;Alexandr A. Kalinin

  • Understanding multicellular function and disease with human tissue-specific networks

    Casey S Greene;Arjun Krishnan;Aaron K Wong;Emanuela Ricciotti

  • The importance of transparency and reproducibility in artificial intelligence research

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

  • An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T. Clark;Asma R. Bankapur

  • The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

    Naihui Zhou;Yuxiang Jiang;Timothy R. Bergquist;Alexandra J. Lee

  • Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing.

    Brett K. Beaulieu-Jones;Zhiwei Steven Wu;Chris Williams;Ran Lee

  • International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways

    Heather J. Cordell;Younghun Han;George F. Mells;Yafang Li

  • Failure to Replicate a Genetic Association May Provide Important Clues About Genetic Architecture

    Casey S. Greene;Nadia M. Penrod;Scott M. Williams;Jason H. Moore

  • An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T Clark;Asma R Bankapur

  • Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders

    Gregory P. Way;Casey S. Greene

  • Defining cell-type specificity at the transcriptional level in human disease.

    Wenjun Ju;Casey S. Greene;Casey S. Greene;Felix Eichinger;Viji Nair

  • Enter the Matrix: Factorization Uncovers Knowledge from Omics

    Genevieve L. Stein-O’Brien;Raman Arora;Aedin C. Culhane;Alexander V. Favorov

  • Big Data Bioinformatics

    Casey S. Greene;Jie Tan;Matthew Ung;Jason H. Moore

  • Constructing knowledge graphs and their biomedical applications.

    David N. Nicholson;Casey S. Greene

  • Semi-supervised learning of the electronic health record for phenotype stratification.

    Brett K Beaulieu-Jones;Casey S Greene

  • Reproducibility standards for machine learning in the life sciences.

    Benjamin J. Heil;Michael M. Hoffman;Florian Markowetz;Su In Lee

  • Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery

    Graciela H. Gonzalez;Tasnia Tahsin;Britton C. Goodale;Anna C. Greene

  • Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

    Gregory P Way;Francisco Sanchez-Vega;Konnor La;Joshua Armenia

  • Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions.

    Casey S Greene;Nadia M Penrod;Jeff Kiralis;Jason H Moore

  • IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks

    Aaron K. Wong;Christopher Y. Park;Casey S. Greene;Lars Ailo Bongo

  • Transparency and reproducibility in artificial intelligence.

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

  • Additional file 1 of An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T. Clark;Asma R. Bankapur

Frequent Co-Authors

Jason H. Moore
Jason H. Moore University of Pennsylvania
Deborah A. Hogan
Deborah A. Hogan Dartmouth College
Jennifer A. Doherty
Jennifer A. Doherty University of Utah
Olga G. Troyanskaya
Olga G. Troyanskaya Princeton University
Christophe Dessimoz
Christophe Dessimoz University College London
Julie M. Gastier-Foster
Julie M. Gastier-Foster Baylor College of Medicine
Predrag Radivojac
Predrag Radivojac Northeastern University
David Haussler
David Haussler University of California, Santa Cruz
David T. Jones
David T. Jones University College London

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