Papers by Chee Keong Kwoh
ACM Transactions on Knowledge Discovery from Data
Unsupervised domain adaptation methods aim at generalizing well on unlabeled test data that may h... more Unsupervised domain adaptation methods aim at generalizing well on unlabeled test data that may have a different (shifted) distribution from the training data. Such methods are typically developed on image data, and their application to time series data is less explored. Existing works on time series domain adaptation suffer from inconsistencies in evaluation schemes, datasets, and backbone neural network architectures. Moreover, labeled target data are often used for model selection, which violates the fundamental assumption of unsupervised domain adaptation. To address these issues, we develop a benchmarking evaluation suite ( AdaTime ) to systematically and fairly evaluate different domain adaptation methods on time series data. Specifically, we standardize the backbone neural network architectures and benchmarking datasets, while also exploring more realistic model selection approaches that can work with no labeled data or just a few labeled samples. Our evaluation includes adap...
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IEEE Transactions on Neural Systems and Rehabilitation Engineering
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A newly emerging novel coronavirus appeared and rapidly spread worldwide and World Health Organiz... more A newly emerging novel coronavirus appeared and rapidly spread worldwide and World Health Organization declared a pandemic on March 11, 2020. The roles and characteristics of coronavirus have captured much attention due to its power of causing a wide variety of infectious diseases, from mild to severe on humans. The detection of the lethality of human coronavirus is key to estimate the viral toxicity and provide perspective for treatment. We developed alignment-free machine learning approaches for an ultra-fast and highly accurate prediction of the lethality of potential human-adapted coronavirus using genomic nucleotide. We performed extensive experiments through six different feature transformation and machine learning algorithms in combination with digital signal processing to infer the lethality of possible future novel coronaviruses using previous existing strains. The results tested on SARS-CoV, MERS-Cov and SARS-CoV-2 datasets show an average 96.7% prediction accuracy. We als...
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IEEE Transactions on Knowledge and Data Engineering
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IEEE Transactions on Emerging Topics in Computational Intelligence
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IEEE Transactions on Neural Networks and Learning Systems
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Computational Science – ICCS 2020, 2020
Feature selection is an important preprocessing step in pattern recognition. In this paper, we pr... more Feature selection is an important preprocessing step in pattern recognition. In this paper, we presented a new feature selection approach in two-class classification problems based on information theory, named minimum Distribution Similarity with Removed Redundancy (mDSRR). Different from the previous methods which use mutual information and greedy iteration with a loss function to rank the features, we rank features according to their distribution similarities in two classes measured by relative entropy, and then remove the high redundant features from the sorted feature subsets. Experimental results on datasets in varieties of fields with different classifiers highlight the value of mDSRR on selecting feature subsets, especially so for choosing small size feature subset. mDSRR is also proved to outperform other state-of-the-art methods in most cases. Besides, we observed that the mutual information may not be a good practice to select the initial feature in the methods with subseq...
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Neurocomputing, 2021
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IEEE Transactions on Industrial Informatics, 2021
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Briefings in Bioinformatics, 2020
Disease–gene association through genome-wide association study (GWAS) is an arduous task for rese... more Disease–gene association through genome-wide association study (GWAS) is an arduous task for researchers. Investigating single nucleotide polymorphisms that correlate with specific diseases needs statistical analysis of associations. Considering the huge number of possible mutations, in addition to its high cost, another important drawback of GWAS analysis is the large number of false positives. Thus, researchers search for more evidence to cross-check their results through different sources. To provide the researchers with alternative and complementary low-cost disease–gene association evidence, computational approaches come into play. Since molecular networks are able to capture complex interplay among molecules in diseases, they become one of the most extensively used data for disease–gene association prediction. In this survey, we aim to provide a comprehensive and up-to-date review of network-based methods for disease gene prediction. We also conduct an empirical analysis on 14...
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IEEE Transactions on Instrumentation and Measurement, 2020
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BMC Genomics, 2019
Background Influenza A virus (IAV) poses threats to human health and life. Many individual studie... more Background Influenza A virus (IAV) poses threats to human health and life. Many individual studies have been carried out in mice to uncover the viral factors responsible for the virulence of IAV infections. Nonetheless, a single study may not provide enough confident about virulence factors, hence combining several studies for a meta-analysis is desired to provide better views. For this, we documented more than 500 records of IAV infections in mice, whose viral proteins could be retrieved and the mouse lethal dose 50 or alternatively, weight loss and/or survival data, was/were available for virulence classification. Results IAV virulence models were learned from various datasets containing aligned IAV proteins and the corresponding two virulence classes (avirulent and virulent) or three virulence classes (low, intermediate and high virulence). Three proven rule-based learning approaches, i.e., OneR, JRip and PART, and additionally random forest were used for modelling. PART models a...
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International Journal of Data Mining and Bioinformatics, 2013
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Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine, 2012
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Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics, 2007
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International Journal of Bio-Science and Bio-Technology, 2014
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Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
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PLoS ONE, 2013
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IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2014
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IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2012
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Papers by Chee Keong Kwoh