World's Best Scientists 2026 revealed!

D-Index & Metrics

Biology and Biochemistry

D-Index
71
Citations
21507
World Ranking
6561
National Ranking
207

Computer Science

D-Index
70
Citations
19975
World Ranking
1873
National Ranking
256

Hong-Bin Shen 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 Hong-Bin Shen 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: 250 publications — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Hong-Bin Shen 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 Hong-Bin Shen 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: 70 D-Index — 87th percentile

87% of scientists in this discipline score the same or lower.

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

Overview

Hong-Bin Shen is affiliated with Shanghai Jiao Tong University in China and has contributed extensively to the field of Biochemistry, Genetics and Molecular Biology, with a particular emphasis on Molecular Biology.

The research focus includes subfields such as Molecular Biology, Computational Theory and Mathematics, Artificial Intelligence, Cancer Research, and Computer Vision and Pattern Recognition.

Key topics covered in the research output include:

  • RNA and protein synthesis mechanisms
  • Protein Structure and Dynamics
  • Machine Learning in Bioinformatics
  • Computational Drug Discovery Methods
  • RNA Research and Splicing
  • Genomics and Phylogenetic Studies
  • Bioinformatics and Genomic Networks

Frequent co-authors collaborating with Hong-Bin Shen are:

  • Xiaoyong Pan
  • Chunqiu Xia
  • Ying Xia
  • Ye Yuan
  • Yi Fang

Publications are regularly found in several venues, including:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Bioinformatics
  • Briefings in Bioinformatics
  • arXiv (Cornell University)
  • Journal of Chemical Information and Modeling

Notable recent papers authored or co-authored by Hong-Bin Shen include:

  • Cell clustering for spatial transcriptomics data with graph neural networks, 2022, Nature Computational Science
  • GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues, 2021, Nucleic Acids Research
  • RBPsuite: RNA-protein binding sites prediction suite based on deep learning, 2020, BMC Genomics
  • lncLocator 2.0: a cell-line-specific subcellular localization predictor for long non-coding RNAs with interpretable deep learning, 2021, Bioinformatics
  • ToxDL: deep learning using primary structure and domain embeddings for assessing protein toxicity, 2020, Bioinformatics

Best Publications

  • Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms.

    Kuo-Chen Chou;Hong-Bin Shen

  • Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization

    Kuo-Chen Chou;Hong-Bin Shen

  • Recent progress in protein subcellular location prediction

    Kuo-Chen Chou;Hong-Bin Shen

  • PseAAC : A flexible web server for generating various kinds of protein pseudo amino acid composition

    Hong-Bin Shen;Kuo-Chen Chou

  • REVIEW : Recent advances in developing web-servers for predicting protein attributes

    Kuo-Chen Chou;Hong-Bin Shen

  • MemType-2L : A Web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM

    Kuo-Chen Chou;Hong-Bin Shen

  • A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0.

    Kuo-Chen Chou;Hong-Bin Shen

  • Ensemble classifier for protein fold pattern recognition

    Hong-Bin Shen;Kuo-Chen Chou

  • Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites.

    Kuo-Chen Chou;Hong-Bin Shen

  • Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms

    Kuo-Chen Chou;Hong-Bin Shen

  • The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier.

    Zhen Cao;Xiaoyong Pan;Yang Yang;Yan Huang

  • Signal-CF: A subsite-coupled and window-fusing approach for predicting signal peptides

    Kuo-Chen Chou;Hong-Bin Shen;Hong-Bin Shen

  • Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-Nearest Neighbor classifiers.

    Kuo-Chen Chou;Hong-Bin Shen

  • EzyPred: A top–down approach for predicting enzyme functional classes and subclasses

    Hong-Bin Shen;Kuo-Chen Chou

  • Signal-3L: A 3-layer approach for predicting signal peptides.

    Hong-Bin Shen;Kuo-Chen Chou

  • Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization.

    Kuo-Chen Chou;Hong-Bin Shen

  • Large‐scale plant protein subcellular location prediction

    Kuo-Chen Chou;Hong-Bin Shen

  • Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites.

    Hong-Bin Shen;Kuo-Chen Chou

  • Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks.

    Xiaoyong Pan;Peter Rijnbeek;Junchi Yan;Hong-Bin Shen

  • Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types.

    Hongbin Shen;Kuo-Chen Chou

  • Nuc-PLoc: a new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM.

    Hong-Bin Shen;Hong-Bin Shen;Kuo-Chen Chou

Frequent Co-Authors

Kuo-Chen Chou
Kuo-Chen Chou The Gordon Life Science Institute
Dong-Jun Yu
Dong-Jun Yu Nanjing University of Science and Technology
Jingyu Yang
Jingyu Yang Nanjing University of Science and Technology
Jiangning Song
Jiangning Song Monash University
Yang Zhang
Yang Zhang University of Michigan–Ann Arbor
Shitong Wang
Shitong Wang Jiangnan University
Tatsuya Akutsu
Tatsuya Akutsu Kyoto University
Xibei Yang
Xibei Yang Jiangsu University of Science and Technology
Geoffrey I. Webb
Geoffrey I. Webb Monash University
Junchi Yan
Junchi Yan Shanghai Jiao Tong University

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