World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
5685
World Ranking
9842
National Ranking
4144

Yaohang Li 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 Yaohang Li 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: 180 publications — 38th percentile

38% of scientists in this discipline score the same or lower.

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

Yaohang Li 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 Yaohang Li 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: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Overview

Yaohang Li is affiliated with Old Dominion University in the United States. Their research primarily spans the fields of Biochemistry, Genetics and Molecular Biology as well as Computer Science. Within these domains, they have contributed extensively to subfields including Molecular Biology, Computational Theory and Mathematics, Artificial Intelligence, Nuclear and High Energy Physics, and Materials Chemistry.

Li's work covers major topics such as Computational Drug Discovery Methods, Machine Learning in Bioinformatics, Protein Structure and Dynamics, Bioinformatics and Genomic Networks, Particle Physics Theoretical and Experimental Studies, Machine Learning in Materials Science, and Gene Expression and Cancer Classification.

The scientist's recent publications have appeared in respected journals and conferences, reflecting active engagement in bioinformatics and computational biology. Notable recent papers include:

  • Biomedical data and computational models for drug repositioning: a comprehensive review, 2020, Briefings in Bioinformatics
  • DeepDTAF: a deep learning method to predict protein-ligand binding affinity, 2021, Briefings in Bioinformatics
  • Computational drug repositioning based on multi-similarities bilinear matrix factorization, 2020, Briefings in Bioinformatics
  • SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning, 2020, Methods
  • BACPI: a bi-directional attention neural network for compound-protein interaction and binding affinity prediction, 2022, Bioinformatics

Frequent co-authors with whom Li has collaborated include Jianxin Wang, Min Li, Qichang Zhao, Mengyun Yang, and Fang-Xiang Wu. Their collaborative work indicates sustained research partnerships within computational biology and related fields.

Li's research has been published frequently in venues such as arXiv (Cornell University), Bioinformatics, Briefings in Bioinformatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, and IEEE Journal of Biomedical and Health Informatics. This reflects a strong presence in both biological and computational research communities.

Best Publications

  • Protein–protein interaction site prediction through combining local and global features with deep neural networks

    Min Zeng;Fuhao Zhang;Fang-Xiang Wu;Yaohang Li

  • Prediction of lncRNA-disease associations based on inductive matrix completion.

    Chengqian Lu;Mengyun Yang;Feng Luo;Fang-Xiang Wu

  • Computational drug repositioning using low-rank matrix approximation and randomized algorithms.

    Huimin Luo;Min Li;Shaokai Wang;Quan Liu

  • Biomedical data and computational models for drug repositioning: a comprehensive review.

    Huimin Luo;Min Li;Mengyun Yang;Fang-Xiang Wu

  • DeepDTAF: a deep learning method to predict protein-ligand binding affinity.

    Kaili Wang;Renyi Zhou;Yaohang Li;Min Li

  • Gaining competitive intelligence from social media data: Evidence from two largest retail chains in the world

    Wu He;Jiancheng Shen;Xin Tian;Yaohang Li

  • Drug repositioning based on bounded nuclear norm regularization

    Mengyun Yang;Mengyun Yang;Huimin Luo;Yaohang Li;Jianxin Wang

  • A Survey of Matrix Completion Methods for Recommendation Systems

    Andy Ramlatchan;Mengyun Yang;Quan Liu;Min Li

  • Automated ICD-9 Coding via A Deep Learning Approach

    Min Li;Zhihui Fei;Min Zeng;Fang-Xiang Wu

  • DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines

    Min Li;Yake Wang;Ruiqing Zheng;Xinghua Shi

  • Clinical big data and deep learning: Applications, challenges, and future outlooks

    Ying Yu;Min Li;Liangliang Liu;Yaohang Li

  • Predicting drugtarget interaction using positive-unlabeled learning

    Wei Lan;Jianxin Wang;Min Li;Jin Liu

  • Identifying At-Risk Students for Early Interventions—A Time-Series Clustering Approach

    Jui-Long Hung;Morgan C. Wang;Shuyan Wang;Maha Abdelrasoul

  • Computational drug repositioning based on multi-similarities bilinear matrix factorization.

    Mengyun Yang;Gaoyan Wu;Qichang Zhao;Yaohang Li

  • A Deep Learning Framework for Identifying Essential Proteins by Integrating Multiple Types of Biological Information

    Min Zeng;Min Li;Zhihui Fei;Fang-Xiang Wu

  • DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions.

    Fuhao Zhang;Hong Song;Min Zeng;Yaohang Li;Yaohang Li

  • United Neighborhood Closeness Centrality and Orthology for Predicting Essential Proteins

    Gaoshi Li;Min Li;Jianxin Wang;Yaohang Li

  • SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning.

    Min Zeng;Chengqian Lu;Fuhao Zhang;Yiming Li

  • Context-based features enhance protein secondary structure prediction accuracy.

    Ashraf Yaseen;Yaohang Li

  • AttentionDTA: prediction of drug–target binding affinity using attention model

    Qichang Zhao;Fen Xiao;Mengyun Yang;Yaohang Li

Frequent Co-Authors

Jianxin Wang
Jianxin Wang Central South University
Min Li
Min Li Central South University
Fang-Xiang Wu
Fang-Xiang Wu University of Saskatchewan
Yi Pan
Yi Pan Shenzhen Institutes of Advanced Technology
Wu He
Wu He Old Dominion University
Sheng Hua Liu
Sheng Hua Liu Central China Normal University
Lukasz Kurgan
Lukasz Kurgan Virginia Commonwealth University
Gui-Rong Liu
Gui-Rong Liu University of Cincinnati
Gongjun Yan
Gongjun Yan University of Southern Indiana
Daniel A. Tortorelli
Daniel A. Tortorelli University of Illinois at Urbana-Champaign

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