World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
69
Citations
17102
World Ranking
1987
National Ranking
272

Zhu-Hong You 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 Zhu-Hong You 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: 326 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Zhu-Hong You 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 Zhu-Hong You 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: 69 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

Zhu-Hong You is affiliated with the Chinese Academy of Sciences in China. Their research spans multiple areas within biochemistry, genetics, molecular biology, and computer science, focusing predominantly on molecular biology and computational methods in biomedicine.

The primary fields of study for Zhu-Hong You include:

  • Biochemistry, Genetics and Molecular Biology
  • Computer Science

Their work dives deeper into specific subfields such as:

  • Molecular Biology
  • Cancer Research
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Materials Chemistry

Research topics Zhu-Hong You has frequently contributed to include:

  • Cancer-related molecular mechanisms research
  • MicroRNA in disease regulation
  • Computational Drug Discovery Methods
  • Bioinformatics and Genomic Networks
  • Circular RNAs in diseases
  • Machine Learning in Bioinformatics
  • RNA modifications and cancer

Some of the recent papers by Zhu-Hong You are:

  • Graph representation learning in bioinformatics: trends, methods and applications, 2021, Briefings in Bioinformatics
  • A survey on computational models for predicting protein-protein interactions, 2021, Briefings in Bioinformatics
  • HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks, 2021, Briefings in Bioinformatics
  • A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network, 2020, BMC Medical Informatics and Decision Making
  • GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm, 2020, PLoS Computational Biology

Zhu-Hong You has collaborated extensively with several frequent co-authors including:

  • Lei Wang
  • Yu-An Huang
  • Lun Hu
  • Bo-Wei Zhao
  • Leon Wong

Their publications appear mostly in well-recognized venues such as:

  • Briefings in Bioinformatics
  • IEEE Journal of Biomedical and Health Informatics
  • BMC Bioinformatics
  • IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • Biology

Best Publications

  • MicroRNAs and complex diseases: from experimental results to computational models.

    Xing Chen;Di Xie;Qi Zhao;Zhu-Hong You

  • Long non-coding RNAs and complex diseases: from experimental results to computational models

    Xing Chen;Chenggang Clarence Yan;Xu Zhang;Zhu-Hong You

  • PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.

    Zhu-Hong You;Zhi-An Huang;Zexuan Zhu;Gui-Ying Yan

  • WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.

    Xing Chen;Chenggang Clarence Yan;Xu Zhang;Zhu Hong You

  • Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis

    Zhu-Hong You;Ying-Ke Lei;Lin Zhu;Junfeng Xia

  • A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method

    Xin Luo;MengChu Zhou;Shuai Li;Zhuhong You

  • Leaf image based cucumber disease recognition using sparse representation classification

    Shanwen Zhang;Xiaowei Wu;Zhuhong You;Liqing Zhang

  • BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction.

    Xing Chen;Di Xie;Lei Wang;Qi Zhao

  • A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases

    Xing Chen;Yu-An Huang;Zhu-Hong You;Gui-Ying Yan

  • Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data

    Zhu-Hong You;Ying-Ke Lei;Jie Gui;De-Shuang Huang

  • Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data

    Xin Luo;MengChu Zhou;Shuai Li;YunNi Xia

  • HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction

    Xing Chen;Chenggang Clarence Yan;Xu Zhang;Zhu-Hong You

  • Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG

    Shanwen Zhang;Haoxiang Wang;Haoxiang Wang;Wenzhun Huang;Zhuhong You

  • DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model

    Hui-Juan Zhu;Zhu-Hong You;Ze-Xuan Zhu;Wei-Lei Shi

  • ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation.

    Hai-Cheng Yi;Zhu-Hong You;Xi Zhou;Li Cheng

  • Graph representation learning in bioinformatics: trends, methods and applications.

    Hai-Cheng Yi;Zhu-Hong You;De-Shuang Huang;Chee Keong Kwoh

  • IRWRLDA: improved random walk with restart for lncRNA-disease association prediction.

    Xing Chen;Zhu-Hong You;Gui-Ying Yan;Dun-Wei Gong

  • MCMDA: Matrix completion for MiRNA-disease association prediction

    Jian-Qiang Li;Zhi-Hao Rong;Xing Chen;Gui-Ying Yan

  • A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network.

    Lei Wang;Zhu-Hong You;Xing Chen;Shi-Xiong Xia

  • HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks

    Unknown

  • Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set

    Zhu-Hong You;Lin Zhu;Chun-Hou Zheng;Hong-Jie Yu

  • Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest.

    Zhu-Hong You;Keith C. C. Chan;Pengwei Hu

Frequent Co-Authors

Xing Chen
Xing Chen Jiangnan University
De-Shuang Huang
De-Shuang Huang Tongji University
Shuai Li
Shuai Li University of Oulu
Lei Wang
Lei Wang University of Wollongong
Keith C. C. Chan
Keith C. C. Chan Hong Kong Polytechnic University
Jianqiang Li
Jianqiang Li Beijing University of Technology
Xin Luo
Xin Luo Chinese Academy of Sciences
Zexuan Zhu
Zexuan Zhu Shenzhen University
MengChu Zhou
MengChu Zhou New Jersey Institute of Technology
Yunni Xia
Yunni Xia Indiana University

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