World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
8978
World Ranking
4908
National Ranking
659

Hui Ding 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 Hui Ding 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: 96 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

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

Hui Ding 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 Hui Ding 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: 53 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Overview

Hui Ding is affiliated with the University of Electronic Science and Technology of China. Their research primarily focuses on biochemistry, genetics, and molecular biology, with a notable emphasis on molecular biology. The scientist's work intersects with subfields such as cancer research, genetics, materials chemistry, and infectious diseases.

The main topics in Hui Ding's research include:

  • Machine Learning in Bioinformatics
  • Genomics and Phylogenetic Studies
  • RNA and protein synthesis mechanisms
  • MicroRNA in disease regulation
  • RNA modifications and cancer
  • Circular RNAs in diseases
  • Cancer-related molecular mechanisms research

Frequent coauthors in their research collaborations include:

  • Fanny Dao
  • Hui Yang
  • Wei Su
  • Hao Lin
  • Hao Lv

Hui Ding has published extensively in the following venues:

  • Briefings in Bioinformatics
  • IEEE Access
  • Frontiers in Genetics
  • Frontiers in Medicine
  • Frontiers in Bioengineering and Biotechnology

Recent notable papers include:

  • "Design powerful predictor for mRNA subcellular location prediction in Homo sapiens," 2020, Briefings in Bioinformatics
  • "iDNA-MS: An Integrated Computational Tool for Detecting DNA Modification Sites in Multiple Genomes," 2020, iScience
  • "Accurately identifying hemagglutinin using sequence information and machine learning methods," 2023, Frontiers in Medicine
  • "TiO2 supported single Ag atoms nanozyme for elimination of SARS-CoV2," 2021, Nano Today
  • "Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design," 2021, Briefings in Bioinformatics

Best Publications

  • Metallic nickel nitride nanosheets realizing enhanced electrochemical water oxidation.

    Kun Xu;Pengzuo Chen;Xiuling Li;Yun Tong

  • Metallic Co4N Porous Nanowire Arrays Activated by Surface Oxidation as Electrocatalysts for the Oxygen Evolution Reaction

    Pengzuo Chen;Kun Xu;Zhiwei Fang;Yun Tong

  • Structural Transformation of Heterogeneous Materials for Electrocatalytic Oxygen Evolution Reaction.

    Hui Ding;Hongfei Liu;Wangsheng Chu;Changzheng Wu

  • iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.

    Hao Lin;En-Ze Deng;Hui Ding;Wei Chen

  • iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition

    Shou-Hui Guo;En-Ze Deng;Li-Qin Xu;Hui Ding

  • iACP: a sequence-based tool for identifying anticancer peptides

    Wei Chen;Hui Ding;Pengmian Feng;Hao Lin

  • iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition.

    Wei Chen;Pengmian Feng;Hui Ding;Hao Lin

  • Phase-Transformation Engineering in Cobalt Diselenide Realizing Enhanced Catalytic Activity for Hydrogen Evolution in an Alkaline Medium.

    Pengzuo Chen;Kun Xu;Shi Tao;Tianpei Zhou

  • iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC.

    Pengmian Feng;Hui Yang;Hui Ding;Hao Lin

  • iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC.

    Pengmian Feng;Hui Ding;Hui Yang;Wei Chen

  • iDNA4mC: identifying DNA N4-methylcytosine sites based on nucleotide chemical properties.

    Wei Chen;Hui Yang;Pengmian Feng;Hui Ding

  • Predicting Subcellular Localization of Mycobacterial Proteins by Using Chous Pseudo Amino Acid Composition

    Hao Lin;Hui Ding;Feng-Biao Guo;An-Ying Zhang

  • iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels.

    Hui Ding;En-Ze Deng;Lu-Feng Yuan;Li Liu

  • Prediction of Cell Wall Lytic Enzymes Using Chous Amphiphilic Pseudo Amino Acid Composition

    Hui Ding;Liaofu Luo;Hao Lin

  • iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition.

    Wei Chen;Wei Chen;Hui Ding;Xu Zhou;Hao Lin

  • iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites.

    Wei Chen;Wei Chen;Pengmian Feng;Hui Yang;Hui Ding

  • iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences.

    Wei Chen;Pengmian Feng;Hui Yang;Hui Ding

  • Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique

    Fu-Ying Dao;Hao Lv;Fang Wang;Chao-Qin Feng

  • iProEP: A Computational Predictor for Predicting Promoter.

    Hong-Yan Lai;Zhao-Yue Zhang;Zhen-Dong Su;Wei Su

  • Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis

    Hui Ding;Peng-Mian Feng;Wei Chen;Hao Lin

  • Understanding Structure-Dependent Catalytic Performance of Nickel Selenides for Electrochemical Water Oxidation

    Kun Xu;Hui Ding;Haifeng Lv;Shi Tao

  • iRNA-2OM: A Sequence-Based Predictor for Identifying 2'-O-Methylation Sites in Homo sapiens.

    Hui Yang;Hao Lv;Hui Ding;Wei Chen;Wei Chen

  • Naïve Bayes Classifier with Feature Selection to Identify Phage Virion Proteins

    Peng-Mian Feng;Hui Ding;Wei Chen;Hao Lin

  • Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition

    Pan-Pan Zhu;Wen-Chao Li;Zhe-Jin Zhong;En-Ze Deng

  • Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition.

    Hao Lin;Hui Ding

  • Identification of Secretory Proteins in Mycobacterium tuberculosis Using Pseudo Amino Acid Composition.

    Huan Yang;Hua Tang;Xin-Xin Chen;Chang-Jian Zhang

  • Design powerful predictor for mRNA subcellular location prediction in Homo sapiens.

    Zhao-Yue Zhang;Yu-He Yang;Hui Ding;Dong Wang

  • Identification of Bacterial Cell Wall Lyases via Pseudo Amino Acid Composition

    Xin-Xin Chen;Hua Tang;Wen-Chao Li;Hao Wu

Frequent Co-Authors

Hao Lin
Hao Lin University of Electronic Science and Technology of China
Wei Chen
Wei Chen Chengdu University of Traditional Chinese Medicine
Yi Xie
Yi Xie University of Science and Technology of China
Changzheng Wu
Changzheng Wu University of Science and Technology of China
Kuo-Chen Chou
Kuo-Chen Chou The Gordon Life Science Institute
Wangsheng Chu
Wangsheng Chu University of Science and Technology of China
Xiaojun Wu
Xiaojun Wu University of Science and Technology of China
Joseph Molnár
Joseph Molnár University of Szeged
Quan Zou
Quan Zou University of Electronic Science and Technology of China
Xiaolong Wang
Xiaolong Wang University of California, San Diego

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