Abstract
Metagenomics analysis has increased its importance in medicine with numerous recent research to investigate and explore the association of metagenomic data to human disease. Discretization approaches are proven as efficient tools to improve the disease prediction performance on metagenomic data. This study proposes a technique based on Entropy and combining some scaler algorithms to conduct bins for discretizing metagenomic data to perform disease classification tasks. Our disease prediction results on six bacterial species abundance metagenomic datasets with the discretization method based on Entropy have revealed promising results compared to the Equal Width Binning with AUCs of 0.955, 0.826, 0.893, 0.692, 0.798, 0.765 classified by a One-dimensional Convolutional Neural Network on data including samples related to Liver Cirrhosis, Colorectal Cancer, Inflammatory Bowel Disease (IBD), and two datasets of Type 2 Diabetes (namely, T2D, and WT2D), respectively.
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Phan, N.Y.K., Tran, T.B., Nguyen, H.H., Nguyen, H.T. (2021). Entropy-Based Discretization Approach on Metagenomic Data for Disease Prediction. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2021. Communications in Computer and Information Science, vol 1500. Springer, Singapore. https://doi.org/10.1007/978-981-16-8062-5_25
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