World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
12409
World Ranking
6711
National Ranking
900

Yun Tang 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 Yun Tang 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: 165 publications — 33rd percentile

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

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

Yun Tang 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 Yun Tang 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: 46 D-Index — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Overview

Yun Tang is affiliated with East China University of Science and Technology in China, with a research portfolio spanning over 230 publications. Their work primarily intersects the fields of computer science and biochemistry, genetics, and molecular biology. Tang's expertise covers a range of subfields, including molecular biology, computational theory and mathematics, pharmacology, artificial intelligence, and spectroscopy.

The scientist's research topics emphasize computational approaches within drug discovery and pharmacology. They have contributed extensively to areas such as computational drug discovery methods, pharmacogenetics and drug metabolism, analytical chemistry and chromatography, metabolomics and mass spectrometry studies, natural language processing techniques, speech recognition and synthesis, and machine learning applications in materials science.

Frequent publication venues for Tang include:

  • Journal of Chemical Information and Modeling
  • arXiv (Cornell University)
  • Chemical Research in Toxicology
  • Briefings in Bioinformatics
  • SSRN Electronic Journal

Key recent publications include:

  • "CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity," 2020, Environmental Health Perspectives
  • "CATMoS: Collaborative Acute Toxicity Modeling Suite," 2021, Environmental Health Perspectives
  • "admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties," 2024, Nucleic Acids Research
  • "Computational Approaches to Identify Structural Alerts and Their Applications in Environmental Toxicology and Drug Discovery," 2020, Chemical Research in Toxicology
  • "MedChatZH: A tuning LLM for traditional Chinese medicine consultations," 2024, Computers in Biology and Medicine

Tang collaborates frequently with several researchers, including Weihua Li, Guixia Liu, Zengrui Wu, Yimeng Wang, and Chaofeng Lou. These collaborations have contributed to a substantial body of work focusing on chemical informatics, toxicology modeling, and biomedical computational methods.

Best Publications

  • admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties.

    Feixiong Cheng;Weihua Li;Yadi Zhou;Jie Shen

  • admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties

    Hongbin Yang;Chaofeng Lou;Lixia Sun;Jie Li

  • Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

    Feixiong Cheng;Chuang Liu;Jing Jiang;Weiqiang Lu

  • ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness.

    Longfei Guan;Hongbin Yang;Yingchun Cai;Lixia Sun

  • Estimation of ADME properties with substructure pattern recognition.

    Jie Shen;Feixiong Cheng;You Xu;Weihua Li

  • In silico ADMET prediction: recent advances, current challenges and future trends.

    Feixiong Cheng;Weihua Li;Guixia Liu;Yun Tang

  • In silico Prediction of Chemical Ames Mutagenicity

    Congying Xu;Feixiong Cheng;Lei Chen;Zheng Du

  • In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts.

    Hongbin Yang;Lixia Sun;Weihua Li;Guixia Liu

  • Network-Based Methods for Prediction of Drug-Target Interactions.

    Zengrui Wu;Weihua Li;Guixia Liu;Yun Tang

  • Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers.

    Feixiong Cheng;Yue Yu;Jie Shen;Lei Yang

  • CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

    Kamel Mansouri;Nicole Kleinstreuer;Ahmed M. Abdelaziz;Domenico Alberga

  • In Silico Prediction of Chemical Acute Oral Toxicity Using Multi-Classification Methods

    Xiao Li;Lei Chen;Feixiong Cheng;Zengrui Wu

  • In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

    Zhuang Wang;Hongbin Yang;Zengrui Wu;Tianduanyi Wang

  • New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery.

    Yun Tang;Weiliang Zhu;Kaixian Chen;Hualiang Jiang;Hualiang Jiang

  • Performance Evaluation of 2D Fingerprint and 3D Shape Similarity Methods in Virtual Screening

    Guoping Hu;Guanglin Kuang;Wen Xiao;Weihua Li

  • SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug–target interactions and drug repositioning

    Zengrui Wu;Feixiong Cheng;Jie Li;Weihua Li

  • Prediction of chemical–protein interactions: multitarget-QSAR versus computational chemogenomic methods

    Feixiong Cheng;Yadi Zhou;Jie Li;Weihua Li

  • Adverse drug events: database construction and in silico prediction.

    Feixiong Cheng;Weihua Li;Xichuan Wang;Yadi Zhou

  • CATMoS: Collaborative Acute Toxicity Modeling Suite.

    Kamel Mansouri;Agnes L. Karmaus;Jeremy Fitzpatrick;Grace Patlewicz

  • In silico assessment of chemical biodegradability.

    Feixiong Cheng;Yutaka Ikenaga;Yadi Zhou;Yue Yu

  • Prediction of Chemical-Protein Interactions Network with Weighted Network-Based Inference Method

    Feixiong Cheng;Yadi Zhou;Weihua Li;Guixia Liu

Frequent Co-Authors

Weihua Li
Weihua Li East China University of Science and Technology
Feixiong Cheng
Feixiong Cheng Case Western Reserve University
Hualiang Jiang
Hualiang Jiang Chinese Academy of Sciences
Xiao-Peng He
Xiao-Peng He East China University of Science and Technology
Xu Shen
Xu Shen Nanjing University of Chinese Medicine
Kaixian Chen
Kaixian Chen Chinese Academy of Sciences
Weiliang Zhu
Weiliang Zhu Chinese Academy of Sciences
Wei Wang
Wei Wang East China University of Science and Technology
Mingyue Zheng
Mingyue Zheng Chinese Academy of Sciences
Yong-Tang Zheng
Yong-Tang Zheng Kunming Institute of Zoology

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Computer Science in the USA opens the door to an array of specialized online degrees and career options. Today, many students are exploring interdisciplinary paths that combine programming skills with fields like engineering, physics, and data science.

For those interested in sustainability, an environmental engineer degree online is a practical choice, preparing graduates to tackle real-world environmental challenges using technology. Meanwhile, students keen on designing innovative mechanical systems may find pursuing the cheapest online master's mechanical engineering program a valuable investment in their future.

Those passionate about research and the theoretical foundations of computing often pursue an online theoretical physics degree, combining the analytical approach of physics with computational problem-solving. In today’s data-driven world, students can also consider the cheapest master in data science to enhance their career prospects in tech, analytics, and machine learning.

These online programs offer flexibility, affordability, and specialization, helping graduates stay competitive in a rapidly-evolving job market.

Best Scientists Citing Yun Tang

Trending Scientists

Recently Published Articles