World's Best Scientists 2026 revealed!

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Computer Science

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

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
Limsoon Wong
Limsoon Wong National University of Singapore
See-Kiong Ng
See-Kiong Ng National University of Singapore
Jiang Liu
Jiang Liu Southern University of Science and Technology
Chang-Dong Wang
Chang-Dong Wang Sun Yat-sen University
Bertil Schmidt
Bertil Schmidt Johannes Gutenberg University of Mainz
Teresa M. Przytycka
Teresa M. Przytycka National Institutes of Health
Vladimir Brusic
Vladimir Brusic University of Nottingham Ningbo China

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