World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
7740
World Ranking
7245
National Ranking
96

Chee Keong Kwoh 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 Chee Keong Kwoh 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: 215 publications — 52nd percentile

52% of scientists in this discipline score the same or lower.

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

Chee Keong Kwoh 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 Chee Keong Kwoh 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Chee Keong Kwoh is affiliated with Nanyang Technological University in Singapore. Their research spans multiple core areas within biochemistry, genetics, molecular biology, and computer science, with notable contributions intersecting both disciplines.

The primary fields of study include:

  • Biochemistry, Genetics and Molecular Biology
  • Computer Science

Their subfields of expertise extend across:

  • Molecular Biology
  • Artificial Intelligence
  • Epidemiology
  • Signal Processing
  • Control and Systems Engineering

Kwoh's work covers a range of prominent research topics such as:

  • Machine Learning in Bioinformatics
  • RNA and protein synthesis mechanisms
  • Influenza Virus Research Studies
  • Domain Adaptation and Few-Shot Learning
  • Bioinformatics and Genomic Networks
  • Advanced Graph Neural Networks
  • Anomaly Detection Techniques and Applications

Recent publications illustrate the interdisciplinary nature of Kwoh's work. Notable papers include:

  • "An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG" (2021) published in IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • "Graph representation learning in bioinformatics: trends, methods and applications" (2021) published in Briefings in Bioinformatics
  • "Predicting human microbe-drug associations via graph convolutional network with conditional random field" (2020) published in Bioinformatics
  • "Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction" (2020) published in IEEE Transactions on Industrial Informatics
  • "Conditional Contrastive Domain Generalization for Fault Diagnosis" (2022) published in IEEE Transactions on Instrumentation and Measurement

Frequent co-authors in Kwoh's research collaborations include:

  • Min Wu
  • Xiaoli Li
  • Zhenghua Chen
  • Mohamed Ragab
  • Emadeldeen Eldele

Their research output is regularly published in venues such as:

  • arXiv (Cornell University)
  • Briefings in Bioinformatics
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Bioinformatics
  • IEEE Transactions on Instrumentation and Measurement

Best Publications

  • An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

    Emadeldeen Eldele;Zhenghua Chen;Chengyu Liu;Min Wu

  • Time-Series Representation Learning via Temporal and Contextual Contrasting

    Emadeldeen Eldele;Mohamed Ragab;Zhenghua Chen;Min Wu

  • A core-attachment based method to detect protein complexes in PPI networks

    Min Wu;Xiaoli Li;Chee Keong Kwoh;See-Kiong Ng

  • Drug–target interaction prediction by learning from local information and neighbors

    Jian-Ping Mei;Chee-Keong Kwoh;Peng Yang;Xiao-Li Li

  • Computational approaches for detecting protein complexes from protein interaction networks: a survey

    Xiaoli Li;Min Wu;Chee-Keong Kwoh;See-Kiong Ng

  • Ultra-Scalable Spectral Clustering and Ensemble Clustering

    Dong Huang;Chang-Dong Wang;Jian-Sheng Wu;Jian-Huang Lai

  • Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration.

    Nafisa Mohamed Hassan;Amr Ali Alhossary;Yuguang Mu;Chee-Keong Kwoh

  • Fast, accurate, and reliable molecular docking with QuickVina 2.

    Amr Alhossary;Stephanus Daniel Handoko;Yuguang Mu;Chee Keong Kwoh

  • Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization

    Ali Ezzat;Peilin Zhao;Min Wu;Xiao-Li Li

  • Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey.

    Ali Ezzat;Min Wu;Xiaoli Li;Chee Keong Kwoh

  • Positive-unlabeled learning for disease gene identification

    Peng Yang;Xiao-Li Li;Jian-Ping Mei;Chee-Keong Kwoh

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

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

  • Enhanced Ensemble Clustering via Fast Propagation of Cluster-Wise Similarities

    Dong Huang;Chang-Dong Wang;Hongxing Peng;Jianhuang Lai

  • Augmented reality systems for medical applications

    Son-Lik Tang;Chee-Keong Kwoh;Ming-Yeong Teo;Ng Wan Sing

  • Predicting human microbe-drug associations via graph convolutional network with conditional random field.

    Yahui Long;Yahui Long;Min Wu;Chee Keong Kwoh;Jiawei Luo

  • Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction

    Mohamed Ragab;Zhenghua Chen;Min Wu;Chuan Sheng Foo

  • CovalentDock: Automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints

    Xuchang Ouyang;Shuo Zhou;Chinh Tran To Su;Zemei Ge

  • Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification

    Unknown

  • MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters.

    Meng Zhang;Fuyi Li;Tatiana T Marquez-Lago;André Leier

  • Drug-target interaction prediction via class imbalance-aware ensemble learning

    Ali Ezzat;Min Wu;Xiaoli Li;Chee Keong Kwoh

  • The safety issues of medical robotics

    Baowei Fei;Wan Sing Ng;Sunita Chauhan;Chee Keong Kwoh

  • Ensemble Positive Unlabeled Learning for Disease Gene Identification

    Peng Yang;Xiaoli Li;Hon-Nian Chua;Chee-Keong Kwoh

  • Inferring gene-phenotype associations via global protein complex network propagation.

    Peng Yang;Xiaoli Li;Min Wu;Chee-Keong Kwoh

  • A survey on computer aided diagnosis for ocular diseases

    Zhuo Zhang;Zhuo Zhang;Ruchir Srivastava;Huiying Liu;Xiangyu Chen

  • Review of tandem repeat search tools: a systematic approach to evaluating algorithmic performance

    Kian Guan Lim;Chee Keong Kwoh;Li Yang Hsu;Adrianto Wirawan

  • Feasibility Structure Modeling: An Effective Chaperone for Constrained Memetic Algorithms

    S D Handoko;Chee Keong Kwoh;Yew-Soon Ong

  • Ensembling graph attention networks for human microbe-drug association prediction.

    Yahui Long;Yahui Long;Min Wu;Yong Liu;Chee Keong Kwoh

  • Pre-training graph neural networks for link prediction in biomedical networks

    Unknown

  • A random forest based computational model for predicting novel lncRNA-disease associations.

    Dengju Yao;Xiaojuan Zhan;Xiaorong Zhan;Chee Keong Kwoh

Frequent Co-Authors

Xiaoli Li
Xiaoli Li Singapore University of Technology and Design
Yulan He
Yulan He King's College London
Chandra S. Verma
Chandra S. Verma Agency for Science, Technology and Research
See-Kiong Ng
See-Kiong Ng National University of Singapore
Limsoon Wong
Limsoon Wong National University of Singapore
Bertil Schmidt
Bertil Schmidt Johannes Gutenberg University of Mainz
Chang-Dong Wang
Chang-Dong Wang Sun Yat-sen University
Jiang Liu
Jiang Liu Southern University of Science and Technology
Teresa M. Przytycka
Teresa M. Przytycka National Institutes of Health
Tien Yin Wong
Tien Yin Wong Tsinghua University

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