World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
63
Citations
14894
World Ranking
2766
National Ranking
1373

Hongfang Liu 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 Hongfang Liu 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: 329 publications — 79th percentile

79% of scientists in this discipline score the same or lower.

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

Hongfang Liu 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 Hongfang Liu 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: 63 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

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

Overview

Hongfang Liu is affiliated with the Mayo Clinic in the United States and has a prolific research output primarily in engineering and materials science, with a strong interdisciplinary focus spanning several subfields.

The main areas of study include engineering and materials science, supported by extensive work in the following subfields:

  • Materials Chemistry
  • Electrical and Electronic Engineering
  • Renewable Energy, Sustainability and the Environment
  • Biomedical Engineering
  • Civil and Structural Engineering

The scientist's research interests cover a range of topics, including:

  • Corrosion Behavior and Inhibition
  • Concrete Corrosion and Durability
  • Hydrogen embrittlement and corrosion behaviors in metals
  • Electrocatalysts for Energy Conversion
  • Advanced battery technologies research
  • Fuel Cells and Related Materials
  • Electrochemical sensors and biosensors

Hongfang Liu has contributed to many publications, with notable frequent publication venues being:

  • Corrosion Science
  • SSRN Electronic Journal
  • Chemical Engineering Journal
  • The Cambridge Structural Database
  • Optics Letters

Among the recent significant papers authored or coauthored by Hongfang Liu are:

  • Digital twins for health: a scoping review, 2024, npj Digital Medicine
  • Preparation of nickel-iron hydroxides by microorganism corrosion for efficient oxygen evolution, 2020, Nature Communications
  • A Zeolitic-Imidazole Frameworks-Derived Interconnected Macroporous Carbon Matrix for Efficient Oxygen Electrocatalysis in Rechargeable Zinc-Air Batteries, 2020, Advanced Materials
  • Two amino acid derivatives as high efficient green inhibitors for the corrosion of carbon steel in CO2-saturated formation water, 2021, Corrosion Science
  • Dextran derivatives as highly efficient green corrosion inhibitors for carbon steel in CO2-saturated oilfield produced water: Experimental and theoretical approaches, 2021, Chemical Engineering Journal

The scientist has collaborated frequently with several coauthors, including:

  • Guangfang Li
  • Tiansui Zhang
  • Zhengyun Wang
  • Hongwei Liu
  • Shencheng Fu

Best Publications

  • CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set

    William C. Reinhold;Margot Sunshine;Margot Sunshine;Hongfang Liu;Sudhir Varma

  • Clinical information extraction applications: A literature review.

    Yanshan Wang;Liwei Wang;Majid Rastegar-Mojarad;Sungrim Moon

  • Overview of BioCreative II gene mention recognition

    Larry Smith;Lorraine K Tanabe;Rie Johnson nee Ando;Cheng-Ju Kuo

  • Overview of BioCreative II gene normalization.

    Alexander A. Morgan;Zhiyong Lu;Xinglong Wang;Aaron M. Cohen

  • The CHEMDNER corpus of chemicals and drugs and its annotation principles.

    Martin Krallinger;Obdulia Rabal;Florian Leitner;Miguel Vazquez

  • A clinical text classification paradigm using weak supervision and deep representation.

    Yanshan Wang;Sunghwan Sohn;Sijia Liu;Feichen Shen

  • A comparison of word embeddings for the biomedical natural language processing

    Yanshan Wang;Sijia Liu;Naveed Afzal;Majid Rastegar-Mojarad

  • CLAMP - a toolkit for efficiently building customized clinical natural language processing pipelines.

    Ergin Soysal;Jingqi Wang;Min Jiang;Yonghui Wu

  • Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality

    Hua Xu;Melinda C Aldrich;Qingxia Chen;Hongfang Liu

  • Building effective defect-prediction models in practice

    A.G. Koru;H. Liu

  • Deep learning and alternative learning strategies for retrospective real-world clinical data

    David Chen;Sijia Liu;Paul Kingsbury;Sunghwan Sohn

  • Gene name ambiguity of eukaryotic nomenclatures

    Lifeng Chen;Hongfang Liu;Carol Friedman

  • An Investigation into the Functional Form of the Size-Defect Relationship for Software Modules

    A.G. Koru;Dongsong Zhang;K. El Emam;Hongfang Liu

  • The gene normalization task in BioCreative III

    Zhiyong Lu;Hung-Yu Kao;Chih-Hsuan Wei;Minlie Huang

  • Automatic Resolution of Ambiguous Terms Based on Machine Learning and Conceptual Relations in the UMLS

    Hongfang Liu;Stephen B. Johnson;Carol Friedman

  • Representing Information in Patient Reports Using Natural Language Processing and the Extensible Markup Language

    Carol Friedman;George Hripcsak;Lyudmila Shagina;Hongfang Liu

  • An information extraction framework for cohort identification using electronic health records.

    Hongfang Liu;Suzette J Bielinski;Sunghwan Sohn;Sean Murphy

  • Framework for a protein ontology.

    Darren A. Natale;Cecilia N. Arighi;Winona C. Barker;Judith A. Blake

  • Clinical concept extraction: A methodology review

    Sunyang Fu;Sunyang Fu;David Chen;Huan He;Sijia Liu

  • Using machine learning for concept extraction on clinical documents from multiple data sources.

    Manabu Torii;Kavishwar B. Wagholikar;Kavishwar B. Wagholikar;Hongfang Liu;Hongfang Liu

  • mRNA and microRNA Expression Profiles of the NCI-60 Integrated with Drug Activities

    Hongfang Liu;Petula D'Andrade;Stephanie Fulmer-Smentek;Philip Lorenzi;Philip Lorenzi

Frequent Co-Authors

Sunghwan Sohn
Sunghwan Sohn Mayo Clinic
Sijia Liu
Sijia Liu Michigan State University
Christopher G. Chute
Christopher G. Chute Johns Hopkins University
Hua Xu
Hua Xu Yale University
Carol Friedman
Carol Friedman Columbia University
William C. Reinhold
William C. Reinhold National Institutes of Health
Yves Pommier
Yves Pommier National Institutes of Health
Cathy H. Wu
Cathy H. Wu University of Delaware
John N. Weinstein
John N. Weinstein The University of Texas MD Anderson Cancer Center

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