World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
6915
World Ranking
8857
National Ranking
1141

Ran Su 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 Ran Su 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: 88 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

Ran Su 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 Ran Su 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: 41 D-Index — 40th percentile

40% of scientists in this discipline score the same or lower.

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

Overview

Ran Su is affiliated with Tianjin University in China and has contributed extensively to the intersection of biochemistry, genetics, molecular biology, and computer science. Their research involves a strong focus on bioinformatics, molecular biology, and artificial intelligence applications within biological contexts.

Their publication record features works in well-regarded academic venues, reflecting a specialized interest in both the computational and biological sciences. Frequent publication venues include:

  • Briefings in Bioinformatics
  • IEEE Journal of Biomedical and Health Informatics
  • Frontiers in Bioengineering and Biotechnology
  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)

Ran Su's primary fields of study encompass:

  • Biochemistry, Genetics and Molecular Biology
  • Computer Science

Within these fields, their work touches on the following subfields:

  • Molecular Biology
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Radiology, Nuclear Medicine and Imaging

The scientist's research topics frequently cover:

  • Machine Learning in Bioinformatics
  • Computational Drug Discovery Methods
  • AI in cancer detection
  • Bioinformatics and Genomic Networks
  • RNA and protein synthesis mechanisms
  • Genomics and Phylogenetic Studies
  • Machine Learning in Materials Science

Ran Su has coauthored works with the following frequent collaborators:

  • Leyi Wei
  • Changming Sun
  • Qiangguo Jin
  • Quan Zou
  • Junru Jin

Notable recent papers include:

  • RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans, 2020, Frontiers in Bioengineering and Biotechnology
  • iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations, 2022, Genome Biology
  • DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis, 2023, Nucleic Acids Research
  • Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework, 2020, Briefings in Bioinformatics
  • Cascade knowledge diffusion network for skin lesion diagnosis and segmentation, 2020, Applied Soft Computing

Best Publications

  • DUNet: A deformable network for retinal vessel segmentation

    Qiangguo Jin;Zhaopeng Meng;Zhaopeng Meng;Tuan D. Pham;Qi Chen

  • RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

    Qiangguo Jin;Zhaopeng Meng;Changming Sun;Leyi Wei

  • ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides.

    Leyi Wei;Chen Zhou;Huangrong Chen;Jiangning Song

  • Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.

    Leyi Wei;Pengwei Xing;Jiancang Zeng;JinXiu Chen

  • Prediction of human protein subcellular localization using deep learning

    Leyi Wei;Leyi Wei;Yijie Ding;Ran Su;Ran Su;Jijun Tang

  • Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response

    Ran Su;Xinyi Liu;Leyi Wei;Quan Zou

  • CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency

    Leyi Wei;PengWei Xing;Ran Su;Gaotao Shi

  • PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning.

    Leyi Wei;Chen Zhou;Ran Su;Quan Zou

  • DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis

    Unknown

  • M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

    Leyi Wei;Huangrong Chen;Ran Su;Ran Su

  • iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations

    Unknown

  • Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species.

    Leyi Wei;Shasha Luan;Luis Augusto Eijy Nagai;Ran Su

  • ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides.

    Bing Rao;Chen Zhou;Guoying Zhang;Ran Su

  • Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

    Ran Su;Jie Hu;Quan Zou;Balachandran Manavalan

  • Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites

    Leyi Wei;Leyi Wei;Ran Su;Ran Su;Bing Wang;Xiuting Li

  • Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE

    Qi Chen;Zhaopeng Meng;Zhaopeng Meng;Xinyi Liu;Qianguo Jin

  • Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework

    Leyi Wei;Wenjia He;Adeel Malik;Ran Su

  • Iterative feature representations improve N4-methylcytosine site prediction.

    Leyi Wei;Ran Su;Shasha Luan;Zhijun Liao

  • CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning.

    Xiaoli Qiang;Chen Zhou;Xiucai Ye;Pu-Feng Du

  • Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data

    Ran Su;Huichen Wu;Bo Xu;Xiaofeng Liu

  • Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods

    Karthikeyan Kandasamy;Jacqueline Kai Chin Chuah;Ran Su;Peng Huang

  • M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species.

    Xiaoli Qiang;Huangrong Chen;Xiucai Ye;Ran Su

  • Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms

    Leyi Wei;Jie Hu;Fuyi Li;Jiangning Song

Frequent Co-Authors

Leyi Wei
Leyi Wei Shandong University
Quan Zou
Quan Zou University of Electronic Science and Technology of China
Changming Sun
Changming Sun Commonwealth Scientific and Industrial Research Organisation
Tuan D. Pham
Tuan D. Pham Queen Mary University of London
Wei Chen
Wei Chen Chengdu University of Traditional Chinese Medicine
Jiangning Song
Jiangning Song Monash University
Jijun Tang
Jijun Tang University of South Carolina

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