World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
71
Citations
16895
World Ranking
1800
National Ranking
65

Fang-Xiang Wu 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 Fang-Xiang Wu 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: 452 publications — 91st percentile

91% of scientists in this discipline score the same or lower.

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

Fang-Xiang Wu 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 Fang-Xiang Wu 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: 71 D-Index — 88th percentile

88% of scientists in this discipline score the same or lower.

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

Overview

Fang-Xiang Wu is affiliated with the University of Saskatchewan in Canada. Their research primarily focuses on Biochemistry, Genetics and Molecular Biology, with 275 publications contributing to this field. Within this broad domain, Wu's work spans several subfields, including Molecular Biology, Cancer Research, Artificial Intelligence, Computational Theory and Mathematics, and Cognitive Neuroscience.

The scientist's research topics encompass:

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

Wu has published extensively in several notable venues. The most frequent publication sources include:

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

Recent publications by Fang-Xiang Wu include:

  • A survey on U-shaped networks in medical image segmentations (2020, Neurocomputing)
  • Biomedical data and computational models for drug repositioning: a comprehensive review (2020, Briefings in Bioinformatics)
  • Diagnosis of Autism Spectrum Disorder Based on Functional Brain Networks with Deep Learning (2020, Journal of Computational Biology)
  • Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF (2022, Artificial Intelligence in Medicine)
  • Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease (2020, Medical Image Analysis)

Collaborations have played a significant role in Wu's research output. Frequent coauthors include Jianxin Wang, Min Li, Xiujuan Lei, Bo Liao, and Yulian Ding, with collaboration counts ranging from 17 to 37 joint works.

Best Publications

  • CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks.

    Yu Tang;Min Li;Jianxin Wang;Yi Pan

  • A survey of MRI-based brain tumor segmentation methods

    Jin Liu;Min Li;Jianxin Wang;Fangxiang Wu

  • A review on machine learning principles for multi-view biological data integration.

    Yifeng Li;Fang-Xiang Wu;Alioune Ngom

  • Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm

    Huimin Luo;Jianxin Wang;Min Li;Junwei Luo

  • A survey on U-shaped networks in medical image segmentations

    Liangliang Liu;Liangliang Liu;Jianhong Cheng;Quan Quan;Fang-Xiang Wu

  • Protein–protein interaction site prediction through combining local and global features with deep neural networks

    Min Zeng;Fuhao Zhang;Fang-Xiang Wu;Yaohang Li

  • Prediction of lncRNA-disease associations based on inductive matrix completion.

    Chengqian Lu;Mengyun Yang;Feng Luo;Fang-Xiang Wu

  • CircR2Disease: a manually curated database for experimentally supported circular RNAs associated with various diseases.

    Chunyan Fan;Xiujuan Lei;Zengqiang Fang;Qinghua Jiang

  • Recurrent Neural Network for Non-Smooth Convex Optimization Problems With Application to the Identification of Genetic Regulatory Networks

    Long Cheng;Zeng-Guang Hou;Yingzi Lin;Min Tan

  • Classification of Alzheimer's Disease Using Whole Brain Hierarchical Network

    Jin Liu;Min Li;Wei Lan;Fang-Xiang Wu

  • LDAP: a web server for lncRNA-disease association prediction

    Wei Lan;Min Li;Kaijie Zhao;Jin Liu

  • Identifying protein complexes and functional modules—from static PPI networks to dynamic PPI networks

    Bolin Chen;Weiwei Fan;Juan Liu;Fang-Xiang Wu

  • Biomedical data and computational models for drug repositioning: a comprehensive review.

    Huimin Luo;Min Li;Mengyun Yang;Fang-Xiang Wu

  • Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks.

    Wei Hao Peng;Wei Hao Peng;Jianxin Wang;Weiping Wang;Qing Liu

  • ClusterViz: a cytoscape APP for cluster analysis of biological network

    Jianxin Wang;Jiancheng Zhong;Gang Chen;Min Li

  • Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey

    Jin Liu;Min Li;Yi Pan;Wei Lan

  • Protein–protein interactions: detection, reliability assessment and applications

    Xiaoqing Peng;Jianxin Wang;Wei Peng;Fang-Xiang Wu

  • Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF

    Unknown

  • Automated ICD-9 Coding via A Deep Learning Approach

    Min Li;Zhihui Fei;Min Zeng;Fang-Xiang Wu

  • SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation

    Ruiqing Zheng;Min Li;Zhenlan Liang;Fang-Xiang Wu;Fang-Xiang Wu

  • Prediction of CircRNA-Disease Associations Using KATZ Model Based on Heterogeneous Networks.

    Chunyan Fan;Xiujuan Lei;Fang-Xiang Wu

  • Modeling gene expression from microarray expression data with state-space equations.

    Fang-Xiang Wu;Wen-Jun Zhang;Anthony J. Kusalik

  • Predicting MicroRNA-Disease Associations Based on Improved MicroRNA and Disease Similarities

    Wei Lan;Jianxin Wang;Min Li;Jin Liu

Frequent Co-Authors

Jianxin Wang
Jianxin Wang Central South University
Min Li
Min Li Central South University
Wenjun Zhang
Wenjun Zhang City University of Hong Kong
Yaohang Li
Yaohang Li Old Dominion University
Guy G. Poirier
Guy G. Poirier Université Laval
Luonan Chen
Luonan Chen Chinese Academy of Sciences
Xiaohua Hu
Xiaohua Hu Drexel University
Aidong Zhang
Aidong Zhang University of Virginia
Min Tan
Min Tan Chinese Academy of Sciences
Long Cheng
Long Cheng Chinese Academy of Sciences

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