Rezultati - Chee-Keong Kwoh

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  1. 1
    An improved random forest-based computational model for predicting novel miRNA-disease associations

    An improved random forest-based computational model for predicting novel miRNA-disease associations od Dengju Yao, Xiaojuan Zhan, Chee-Keong Kwoh

    Izdano 2019
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  2. 2
    A core-attachment based method to detect protein complexes in PPI networks

    A core-attachment based method to detect protein complexes in PPI networks od Min Wu, Xiaoli Li, Chee Keong Kwoh, See-Kiong Ng

    Izdano 2009
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  3. 3
    Computational approaches for detecting protein complexes from protein interaction networks: a survey

    Computational approaches for detecting protein complexes from protein interaction networks: a survey od Xiaoli Li, Min Wu, Chee-Keong Kwoh, See-Kiong Ng

    Izdano 2010
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  4. 4
    Quantitative model of R-loop forming structures reveals a novel level of RNA-DNA interactome complexity

    Quantitative model of R-loop forming structures reveals a novel level of RNA-DNA interactome complexity od Thidathip Wongsurawat, Piroon Jenjaroenpun, Chee Keong Kwoh, Vladimir A. Kuznetsov

    Izdano 2011
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  5. 5
    Gene, Environment and Methylation (GEM): a tool suite to efficiently navigate large scale epigenome wide association studies and integrate genotype and interaction between genotype and environment

    Gene, Environment and Methylation (GEM): a tool suite to efficiently navigate large scale epigenome wide association studies and integrate genotype and interaction between genotype... od Hong Pan, Joanna D. Holbrook, Neerja Karnani, Chee Keong Kwoh

    Izdano 2016
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  6. 6
    Predicting human microbe–drug associations via graph convolutional network with conditional random field

    Predicting human microbe–drug associations via graph convolutional network with conditional random field od Yahui Long, Min Wu, Chee Keong Kwoh, Jiawei Luo, Xiaoli Li

    Izdano 2020
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  7. 7
    Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation

    Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation od Peng Yang, Xiaoli Li, Min Wu, Chee-Keong Kwoh, See-Kiong Ng

    Izdano 2011
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  8. 8
    Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model

    Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model od Rui Yin, Viet Hung Tran, Xinrui Zhou, Jie Zheng, Chee Keong Kwoh

    Izdano 2018
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  9. 9
    Ensemble Positive Unlabeled Learning for Disease Gene Identification

    Ensemble Positive Unlabeled Learning for Disease Gene Identification od Peng Yang, Xiaoli Li, Hon-Nian Chua, Chee-Keong Kwoh, See-Kiong Ng

    Izdano 2014
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  10. 10
    Positive-unlabeled learning for disease gene identification

    Positive-unlabeled learning for disease gene identification od Peng Yang, Xiaoli Li, Jian-Ping Mei, Chee-Keong Kwoh, See-Kiong Ng

    Izdano 2012
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  11. 11
    A random forest based computational model for predicting novel lncRNA-disease associations

    A random forest based computational model for predicting novel lncRNA-disease associations od Dengju Yao, Xiaojuan Zhan, Xiaorong Zhan, Chee Keong Kwoh, Li Peng, Jinke Wang

    Izdano 2020
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  12. 12
    Self-Supervised Learning for Label- Efficient Sleep Stage Classification: A Comprehensive Evaluation

    Self-Supervised Learning for Label- Efficient Sleep Stage Classification: A Comprehensive Evaluation od Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li

    Izdano 2023
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  13. 13
    ARG-walker: inference of individual specific strengths of meiotic recombination hotspots by population genomics analysis

    ARG-walker: inference of individual specific strengths of meiotic recombination hotspots by population genomics analysis od Hao Chen, Peng Yang, Jing Guo, Chee Keong Kwoh, Teresa M. Przytycka, Jie Zheng

    Izdano 2015
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  14. 14
    Recent advances in network-based methods for disease gene prediction

    Recent advances in network-based methods for disease gene prediction od Sezin Kircali Ata, Min Wu, Yuan Fang, Le Ou-Yang, Chee Keong Kwoh, Xiaoli Li

    Izdano 2020
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  15. 15
    Graph contextualized attention network for predicting synthetic lethality in human cancers

    Graph contextualized attention network for predicting synthetic lethality in human cancers od Yahui Long, Min Wu, Yong Liu, Jie Zheng, Chee Keong Kwoh, Jiawei Luo, Xiaoli Li

    Izdano 2021
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  16. 16
    An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

    An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG od Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee Keong Kwoh, Xiaoli Li, Cuntai Guan

    Izdano 2021
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  17. 17
    Classifying Vulnerability to Sleep Deprivation Using Baseline Measures of Psychomotor Vigilance

    Classifying Vulnerability to Sleep Deprivation Using Baseline Measures of Psychomotor Vigilance od Amiya Patanaik, Chee Keong Kwoh, E.C.-P. Chua, Joshua J. Gooley, Michael W.L. Chee

    Izdano 2015
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  18. 18
    MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters

    MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters od Meng Zhang, Fuyi Li, Tatiana T. Marquez‐Lago, André Leier, Cunshuo Fan, Chee Keong Kwoh, Kuo‐Chen Chou, Jiangning Song, Cangzhi Jia

    Izdano 2019
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  19. 19
    A survey on computer aided diagnosis for ocular diseases

    A survey on computer aided diagnosis for ocular diseases od Zhuo Zhang, Ruchir Srivastava, Hui‐Ying Liu, Xiangyu Chen, Lixin Duan, Damon Wing Kee Wong, Chee Keong Kwoh, Tien Yin Wong, Jiang Liu

    Izdano 2014
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  20. 20
    Simulations of mutant p53 DNA binding domains reveal a novel druggable pocket

    Simulations of mutant p53 DNA binding domains reveal a novel druggable pocket od Mohan R. Pradhan, Jia Wei Siau, Srinivasaraghavan Kannan, Minh N. Nguyen, Zohra Ouaray, Chee Keong Kwoh, David P. Lane, Farid J. Ghadessy, Chandra Verma

    Izdano 2019
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