World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
4968
World Ranking
11735
National Ranking
1453

Zhihao Yang 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 Zhihao Yang 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: 247 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.

Zhihao Yang 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 Zhihao Yang 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Zhihao Yang is affiliated with Dalian University of Technology in China. Their research spans several interconnected fields, primarily focusing on computer science and biochemistry, genetics, and molecular biology. They have contributed extensively to artificial intelligence and molecular biology, alongside notable work in materials chemistry, biomedical engineering, and computer vision and pattern recognition.

Their scientific publications frequently address topics such as topic modeling, biomedical text mining and ontologies, natural language processing techniques, advanced text analysis techniques, machine learning applied to bioinformatics, advanced graph neural networks, and bioinformatics and genomic networks.

Yang has published multiple papers in respected journals, including:

  • Biomedical named entity recognition using BERT in the machine reading comprehension framework (2021), Journal of Biomedical Informatics
  • A neural network-based joint learning approach for biomedical entity and relation extraction from biomedical literature (2020), Journal of Biomedical Informatics
  • A multi-view network for real-time emotion recognition in conversations (2021), Knowledge-Based Systems
  • Multimodal reasoning based on knowledge graph embedding for specific diseases (2022), Bioinformatics
  • Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks (2024), Journal of the American Medical Informatics Association

The main venues where Yang has published regularly include:

  • Journal of Biomedical Informatics
  • arXiv (Cornell University)
  • Knowledge-Based Systems
  • BMC Bioinformatics
  • JMIR Medical Informatics

They frequently collaborate with notable coauthors such as Hongfei Lin, Jian Wang, Yijia Zhang, Lei Wang, and Guangyuan Tian.

Their research bridges computational methods and biomedical applications, involving advanced machine learning techniques, neural networks, and knowledge representation approaches. The topics of recent papers reflect a focus on natural language processing in biomedical contexts, real-time emotion recognition, and the application of knowledge graphs for disease understanding.

Best Publications

  • BioWordVec, improving biomedical word embeddings with subword information and MeSH.

    Yijia Zhang;Yijia Zhang;Qingyu Chen;Zhihao Yang;Hongfei Lin

  • An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition

    Ling Luo;Zhihao Yang;Pei Yang;Yin Zhang

  • Drug Drug Interaction Extraction from Biomedical Literature Using Syntax Convolutional Neural Network

    Zhehuan Zhao;Zhihao Yang;Ling Luo;Hongfei Lin

  • Drug-drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths.

    Yijia Zhang;Wei Zheng;Wei Zheng;Hongfei Lin;Jian Wang

  • A hybrid model based on neural networks for biomedical relation extraction.

    Yijia Zhang;Hongfei Lin;Zhihao Yang;Jian Wang

  • An attention-based effective neural model for drug-drug interactions extraction

    Wei Zheng;Wei Zheng;Hongfei Lin;Ling Luo;Zhehuan Zhao

  • SemaTyP: a knowledge graph based literature mining method for drug discovery

    Shengtian Sang;Zhihao Yang;Lei Wang;Xiaoxia Liu

  • A neural network-based joint learning approach for biomedical entity and relation extraction from biomedical literature

    Ling Luo;Zhihao Yang;Mingyu Cao;Lei Wang

  • Neural network-based approaches for biomedical relation classification: A review.

    Yijia Zhang;Hongfei Lin;Zhihao Yang;Jian Wang

  • Incorporating rich background knowledge for gene named entity classification and recognition

    Yanpeng Li;Hongfei Lin;Zhihao Yang

  • A protein-protein interaction extraction approach based on deep neural network

    Zhehuan Zhao;Zhihao Yang;Hongfei Lin;Jian Wang

  • A multi-view network for real-time emotion recognition in conversations

    Hui Ma;Jian Wang;Hongfei Lin;Xuejun Pan

  • GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From Biomedical Literatures

    Shengtian Sang;Zhihao Yang;Xiaoxia Liu;Lei Wang

  • BioPPISVMExtractor: A protein-protein interaction extractor for biomedical literature using SVM and rich feature sets

    Zhihao Yang;Hongfei Lin;Yanpeng Li

  • Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks

    Unknown

  • Extracting Drug-Drug Interaction from the Biomedical Literature Using a Stacked Generalization-Based Approach

    Linna He;Zhihao Yang;Zhehuan Zhao;Hongfei Lin

  • Multiple kernel learning in protein-protein interaction extraction from biomedical literature

    Zhihao Yang;Nan Tang;Xiao Zhang;Hongfei Lin

  • Adverse drug reaction detection via a multihop self-attention mechanism

    Tongxuan Zhang;Hongfei Lin;Yuqi Ren;Liang Yang

  • Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text

    Zhiheng Li;Zhihao Yang;Chen Shen;Jun Xu

  • Brief Communication: Exploiting the performance of dictionary-based bio-entity name recognition in biomedical literature

    Zhihao Yang;Hongfei Lin;Yanpeng Li

  • Disease named entity recognition from biomedical literature using a novel convolutional neural network.

    Zhehuan Zhao;Zhihao Yang;Ling Luo;Lei Wang

  • A method for predicting protein complex in dynamic PPI networks.

    Yijia Zhang;Hongfei Lin;Zhihao Yang;Jian Wang

  • Opinion Mining in e-Learning System

    Dan Song;Hongfei Lin;Zhihao Yang

Frequent Co-Authors

Lei Wang
Lei Wang University of Wollongong
Feng Xia
Feng Xia RMIT University
Lei Wang
Lei Wang University of California, San Francisco
Bo Xu
Bo Xu Nanjing University of Science and Technology
Xiaodong Liu
Xiaodong Liu Dalian University of Technology
Zhiyong Lu
Zhiyong Lu National Institutes of Health
Xiaohua Hu
Xiaohua Hu Drexel University
Yu Liu
Yu Liu University of Electronic Science and Technology of China
Michel Dumontier
Michel Dumontier Maastricht University
Xiangjie Kong
Xiangjie Kong Zhejiang University of Technology

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