World's Best Scientists 2026 revealed!

D-Index & Metrics

Biology and Biochemistry

D-Index
104
Citations
49475
World Ranking
1234
National Ranking
730

Jason H. Moore publication distribution in Biology and Biochemistry in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Biology and Biochemistry in 2026. The highlighted bar marks where Jason H. Moore sits on this spectrum.

47–56 publications: 8 scientists 57–66 publications: 35 scientists 67–76 publications: 106 scientists 77–86 publications: 231 scientists 87–96 publications: 414 scientists 97–106 publications: 546 scientists 107–116 publications: 704 scientists 117–126 publications: 849 scientists 127–136 publications: 980 scientists 137–146 publications: 942 scientists 147–156 publications: 969 scientists 157–166 publications: 950 scientists 167–176 publications: 951 scientists 177–186 publications: 915 scientists 187–196 publications: 787 scientists 197–206 publications: 841 scientists 207–216 publications: 735 scientists 217–226 publications: 709 scientists 227–236 publications: 651 scientists 237–246 publications: 605 scientists 247–256 publications: 510 scientists 257–266 publications: 524 scientists 267–276 publications: 434 scientists 277–286 publications: 418 scientists 287–296 publications: 350 scientists 297–306 publications: 363 scientists 307–316 publications: 315 scientists 317–326 publications: 296 scientists 327–336 publications: 261 scientists 337–346 publications: 240 scientists 347–356 publications: 219 scientists 357–366 publications: 197 scientists 367–376 publications: 154 scientists 377–386 publications: 161 scientists 387–396 publications: 155 scientists 397–406 publications: 145 scientists 407–416 publications: 124 scientists 417–426 publications: 112 scientists 427–436 publications: 132 scientists 437–446 publications: 116 scientists 447–456 publications: 99 scientists 457–466 publications: 81 scientists 467–476 publications: 91 scientists 477–486 publications: 80 scientists 487–496 publications: 80 scientists 497–506 publications: 60 scientists 507–516 publications: 36 scientists 517–526 publications: 46 scientists 527–536 publications: 54 scientists 537–546 publications: 44 scientists 547–556 publications: 43 scientists 557–566 publications: 43 scientists 567–576 publications: 42 scientists 577–586 publications: 25 scientists 587–596 publications: 34 scientists 597–606 publications: 23 scientists 607–616 publications: 33 scientists 617–626 publications: 31 scientists 627–636 publications: 27 scientists 637–646 publications: 25 scientists 647–656 publications: 28 scientists 657–666 publications: 34 scientists 667–676 publications: 18 scientists 677–686 publications: 16 scientists 687–696 publications: 10 scientists 697–706 publications: 12 scientists 707–716 publications: 21 scientists 717–726 publications: 12 scientists 727–736 publications: 12 scientists 737–746 publications: 10 scientists 747–756 publications: 7 scientists 757–766 publications: 13 scientists 767–776 publications: 15 scientists 777–786 publications: 13 scientists 787–796 publications: 9 scientists 797–806 publications: 9 scientists 807–816 publications: 7 scientists 817–826 publications: 4 scientists 827–836 publications: 9 scientists 837–846 publications: 7 scientists 847–856 publications: 3 scientists 857–866 publications: 5 scientists 867–876 publications: 5 scientists 877–886 publications: 11 scientists 887–896 publications: 3 scientists 897–906 publications: 4 scientists 907–916 publications: 7 scientists 917–926 publications: 5 scientists 927–936 publications: 6 scientists 937–946 publications: 6 scientists 947–956 publications: 3 scientists 957–966 publications: 7 scientists 967–976 publications: 2 scientists 977–986 publications: 2 scientists 987–996 publications: 1 scientists 997–1,006 publications: 5 scientists 1,007–1,016 publications: 2 scientists 1,017–1,026 publications: 2 scientists 1,027 publications: 1 scientists 1,028+ publications: 100 scientists
47 publications 1,028+

This scientist: 713 publications — 98th percentile

98% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,028 publications or more.

Jason H. Moore D-index placement in Biology and Biochemistry in 2026

The chart shows the D-index (discipline H-index) distribution of Biology and Biochemistry scientists ranked by Research.com in 2026. The highlighted bar marks where Jason H. Moore sits on this spectrum.

40–41 D-Index: 80 scientists 42–43 D-Index: 183 scientists 44–45 D-Index: 317 scientists 46–47 D-Index: 504 scientists 48–49 D-Index: 718 scientists 50–51 D-Index: 900 scientists 52–53 D-Index: 1,026 scientists 54–55 D-Index: 1,150 scientists 56–57 D-Index: 1,236 scientists 58–59 D-Index: 1,253 scientists 60–61 D-Index: 1,163 scientists 62–63 D-Index: 1,131 scientists 64–65 D-Index: 1,032 scientists 66–67 D-Index: 897 scientists 68–69 D-Index: 814 scientists 70–71 D-Index: 715 scientists 72–73 D-Index: 709 scientists 74–75 D-Index: 596 scientists 76–77 D-Index: 512 scientists 78–79 D-Index: 473 scientists 80–81 D-Index: 412 scientists 82–83 D-Index: 373 scientists 84–85 D-Index: 358 scientists 86–87 D-Index: 285 scientists 88–89 D-Index: 273 scientists 90–91 D-Index: 227 scientists 92–93 D-Index: 208 scientists 94–95 D-Index: 193 scientists 96–97 D-Index: 153 scientists 98–99 D-Index: 157 scientists 100–101 D-Index: 148 scientists 102–103 D-Index: 120 scientists 104–105 D-Index: 113 scientists 106–107 D-Index: 100 scientists 108–109 D-Index: 86 scientists 110–111 D-Index: 67 scientists 112–113 D-Index: 72 scientists 114–115 D-Index: 73 scientists 116–117 D-Index: 64 scientists 118–119 D-Index: 53 scientists 120–121 D-Index: 60 scientists 122–123 D-Index: 54 scientists 124–125 D-Index: 43 scientists 126–127 D-Index: 38 scientists 128–129 D-Index: 49 scientists 130–131 D-Index: 26 scientists 132–133 D-Index: 18 scientists 134–135 D-Index: 23 scientists 136–137 D-Index: 32 scientists 138–139 D-Index: 32 scientists 140–141 D-Index: 27 scientists 142–143 D-Index: 19 scientists 144–145 D-Index: 22 scientists 146–147 D-Index: 12 scientists 148–149 D-Index: 16 scientists 150–151 D-Index: 14 scientists 152–153 D-Index: 10 scientists 154–155 D-Index: 13 scientists 156–157 D-Index: 10 scientists 158–159 D-Index: 7 scientists 160–161 D-Index: 9 scientists 162–163 D-Index: 13 scientists 164–165 D-Index: 4 scientists 166 D-Index: 4 scientists 167+ D-Index: 98 scientists
40 D-Index 167+

This scientist: 104 D-Index — 94th percentile

94% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 167 D-Index or more.

Research.com Recognitions

  • 2011 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

Jason H. Moore is affiliated with the University of Pennsylvania in the United States. Their research encompasses multiple intersecting fields including Computer Science, Biochemistry, Genetics and Molecular Biology, and Medicine.

The scientist's work spans several specialized subfields such as Artificial Intelligence, Genetics, Molecular Biology, Radiology, Nuclear Medicine and Imaging, and Health Information Management. Their research intersects a diverse set of topics with notable coverage in Genetic Associations and Epidemiology, Bioinformatics and Genomic Networks, Evolutionary Algorithms and Applications, Machine Learning and Data Classification, Machine Learning in Healthcare, Metaheuristic Optimization Algorithms Research, and Artificial Intelligence in Healthcare and Education.

Jason H. Moore has published extensively, with a particularly high frequency in certain scientific venues. These include bioRxiv (Cold Spring Harbor Laboratory), BioData Mining, arXiv (Cornell University), Scientific Reports, and Bioinformatics.

The scientist has collaborated frequently with a number of researchers, among them Marylyn D. Ritchie, Ryan J. Urbanowicz, Joseph D. Romano, Li Shen, and John H. Holmes.

Among recent publications are:

  • ChatGPT and large language models in academia: opportunities and challenges (2023, BioData Mining)
  • Multiple Plasma Biomarkers for Risk Stratification in Patients With Heart Failure and Preserved Ejection Fraction (2020, Journal of the American College of Cardiology)
  • Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes (2022, Nature Metabolism)
  • Contemporary Symbolic Regression Methods and their Relative Performance (2021, PubMed)
  • A manifesto on explainability for artificial intelligence in medicine (2022, Artificial Intelligence in Medicine)

The contributions of Jason H. Moore extend into machine learning applications in healthcare, bioinformatics networks, and evolutionary optimization methods. This positions the scientist at the intersection of computational and medical research domains.

Jason H. Moore was recognized as a Fellow of the American Association for the Advancement of Science (AAAS) in 2011.

Best Publications

  • Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer

    Marylyn D. Ritchie;Lance W. Hahn;Nady Roodi;L. Renee Bailey

  • Missing heritability and strategies for finding the underlying causes of complex disease

    Evan E. Eichler;Jonathan Flint;Greg Gibson;Augustine Kong

  • The Genetic Structure and History of Africans and African Americans

    Sarah A. Tishkoff;Floyd A. Reed;Françoise R. Friedlaender;Christopher Ehret

  • Chapter 11: Genome-wide association studies.

    William S. Bush;Jason H. Moore

  • Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions.

    Lance W. Hahn;Marylyn D. Ritchie;Jason H. Moore

  • Relief-based feature selection: Introduction and review.

    Ryan J. Urbanowicz;Melissa Meeker;William G. La Cava;Randal S. Olson

  • Characterization of MicroRNA Expression Levels and Their Biological Correlates in Human Cancer Cell Lines

    Arti Gaur;David A. Jewell;Yu Liang;Dana Ridzon

  • The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases

    Jason H. Moore

  • Proteomic patterns of tumour subsets in non-small-cell lung cancer.

    Kiyoshi Yanagisawa;Yu Shyr;Baogang J Xu;Pierre P Massion

  • TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning

    Randal S. Olson;Jason H. Moore

  • A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility

    Jason H. Moore;Joshua C. Gilbert;Chia-Ti Tsai;Fu-Tien Chiang

  • Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity

    Marylyn D. Ritchie;Lance W. Hahn;Jason H. Moore

  • Bioinformatics challenges for genome-wide association studies

    Jason H. Moore;Folkert W. Asselbergs;Scott M. Williams

  • Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

    Randal S. Olson;Nathan Bartley;Ryan J. Urbanowicz;Jason H. Moore

  • Renin-Angiotensin System Gene Polymorphisms and Atrial Fibrillation

    Chia Ti Tsai;Ling Ping Lai;Jiunn Lee Lin;Fu Tien Chiang

  • A High-Density Admixture Map for Disease Gene Discovery in African Americans

    Michael W. Smith;Michael W. Smith;Nick Patterson;James A. Lautenberger;Ann L. Truelove;Ann L. Truelove

  • Alzheimer's Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans.

    Andrew J. Saykin;Li Shen;Tatiana M. Foroud;Steven G. Potkin

  • New strategies for identifying gene-gene interactions in hypertension

    Jason H Moore;Scott M Williams

  • A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction

    Digna R. Velez;Bill C. White;Alison A. Motsinger;William S. Bush

  • Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort

    Li Shen;Sungeun Kim;Shannon L. Risacher;Kwangsik Nho

Frequent Co-Authors

Scott M. Williams
Scott M. Williams Case Western Reserve University
Marylyn D. Ritchie
Marylyn D. Ritchie University of Pennsylvania
Casey S. Greene
Casey S. Greene University of Colorado Denver
Andrew J. Saykin
Andrew J. Saykin Indiana University
Li Shen
Li Shen University of Pennsylvania
Moshe Sipper
Moshe Sipper Ben-Gurion University of the Negev
Margaret R. Karagas
Margaret R. Karagas Dartmouth College
Shannon L. Risacher
Shannon L. Risacher Indiana University
Kwangsik Nho
Kwangsik Nho Indiana University

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