• Phan Tấn Tài Cantho University, Viet Nam
  • Tạ Đặng Vĩnh Phúc Cantho University, Viet Nam
  • Phan Nguyễn Minh Thảo Cantho University, Viet Nam
  • Nguyễn Thị Ngọc Chăm Cantho University, Viet Nam
  • Đào Công Tính Cantho University, Viet Nam
  • Phạm Huỳnh Ngọc Cantho University, Viet Nam
  • Nguyễn Thanh Hải Cantho University, Viet Nam




Deep Learning, Disease diagnosis, Gene Analysis, Machine Learning, Metagenomic, Personalized Medicine.


In recent years, Metagenomic data, or “multi-genome” data, has been increasingly used for research in “personalized medicine” approaches with the purpose of improving and enhancing effectiveness in human health care. Many studies have experimentally analyzed this data and proposed many methods to improve the accuracy of the analysis. Applying and integrating information technology to process and analyze Metagenomic data for personalized medicine approaches are necessary because of the enormous complexity of Metagenomic data. The potential advantages of Metagenomic data have been proven through many studies. Within the scope of this research, we introduce and evaluate useful tools for studying Metagenomic data in supporting the diagnosis of human disease and health conditions. From these studies, we may develop extensive and in-depth studies from previous studies to explore the important effect of the microbial ecosystem that is a rich set of microbial features for prediction and biomarker discovery in the human body. Moreover, there are trends diagnosis, appropriate treatments to improve and enhance human health.


Download data is not yet available.


Abubucker, S., Segata, N., Goll, J., Schubert, A. M., Izard, J., Cantarel, … Huttenhower, C. (2012). Metabolic reconstruction for Metagenomic data and its application to the human microbiome. PLOS Computational Biology, 8(6), 1-17.

Bajaj, J. S., Betrapally, N. S., & Gillevet, P. M. (2015). Decompensated cirrhosis and microbiome interpretation. Nature, 525(7569), 1-4.

Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.

Breiman, L., & Cutler, A. (2012). Breiman and Cutler’s random forests for classification and regression (Package randomForest). Retrieved from http://math.furman.edu/~dcs/courses/math47/R/library/randomForest/html/00Index.html.

Cai, L., Wu, H., Li, D., Zhou, K., & Zou, F. (2015). Type 2 diabetes biomarkers of human gut microbiota selected via iterative sure independent screening method. PloS One, 10(10), 1-15.

Chatelier, L. E., Nielsen, T., Qin, J., Prifti, E., Hildebrand, F., Falony, G., … Pedersen, O. (2013). Richness of human gut microbiome correlates with metabolic markers. Nature, 500(7464), 541-546.

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273-297.

Darling, A. E., Jospin, G., Lowe, E., Matsen, IV. F. A., Bik, H. M., & Eisen, J. A. (2014). PhyloSift: phylogenetic analysis of genomes and metagenomes. PeerJ, 2013(1), 1-28.

Dinsdale, E. A., Edwards, R. A., Bailey, B. A., Tuba, I., Akhter, S., McNair, K., … Ponomarenko, V. (2013). Multivariate Analysis of Functional Metagenomes. Frontiers, 4(41), 1-25.

Ditzler, G., Polikar, R., & Rosen, G. (2015). Multi-layer and recursive neural networks for metagenomic classification. IEEE Transactions on NanoBioscience, 14(6), 608-616.

Ditzler, G., Morrison, J. C., Lan, Y., & Rosen, G. L. (2015). Feature subset selection for metagenomics. BMC Bioinformatics, 16, 1-8.

Dodge, S., & Karam, L. (2017). A study and comparison of human and deep learning recognition performance under visual distortions. New York, US: Institute of Electrical and Electronics Engineers Inc Publishing.

Dudley, J. T., & Karczewski, K. J. (2014). Exploring personal genomics. Oxford, UK: Oxford University Press Publishing.

Ehrlich, S. D. (2016). The human gut microbiome impacts health and disease. Comptes Rendus Biologies, 339(7-8), 319-323.

Forslund, K., Hildebrand, F., Nielsen, T., Falony, G., Chatelier, L. E., Sunagawa, S., … Pedersen, O. (2015). Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature, 528, 262-266.

Gevers, D., Kugathasan, S., Denson, LA., Vázquez-Baeza, Y., Van, T. W., Ren, B., ... Crandall, W. (2014). The treatment naïve microbiome in new-onset Crohn’s disease. Cell Host Microbe, 15(3), 382-392.

Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507.

Hicilar, H., Nalbantoglu, O. U., Aran, O., & Bakir-Gungor, B. (2020). Inflammatory Bowel Disease Biomarkers of Human Gut Microbiota Selected via Ensemble Feature Selection Methods. Retrieved from https://www.semanticscholar.org/


Jiang, Y., Wang, J., Xia, D., & Yu, G. (2017). EnSVMB: Metagenomics Fragments Classification using Ensemble SVM and BLAST. Scientific Reports, 7(1), 1-10.

Karlsson, F. H., Tremaroli, V., Nookaew, I., Bergström, G., Behre, C. J., Fagerberg, B., … Bäckhed, F. (2013). Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature, 498(7452), 99-103.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In D. S. Touretzky (Ed), Advances in neural information processing systems 2 (pp. 1097-1105). Vancouver, Canada: Neural Information Processing Systems Publishing.

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1990). Handwritten digit recognition with a back-propagation network. In D. S. Touretzky (Ed), Advances in neural information processing systems 2 (pp. 396-404). Vancouver, Canada: Neural Information Processing Systems Publishing.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Lin, Y. C. (2015). A new binning method for metagenomics by one-dimensional cellular automata. International Journal of Genomics, 2015, 1-6.

Lo, C., & Marculescu, R. (2018). Accurate classification of host phenotypes from metagenomic data using neural networks. BMC Bioinformatics, 20, 1-14.

Nguyen, T. H., Prifti, E., Chevaleyre, Y., Sokolovska, N., & Zucker, J. D. (2018). Disease Classification in Metagenomics with 2D Embeddings and Deep Learning. Retrieved from https://arxiv.org/abs/1806.09046.

Nguyen, T. H., Prifti, E., Sokolovska, N., & Zucker, J. D. (2019). Disease Prediction Using Synthetic Image Representations of Metagenomic Data and Convolutional Neural Networks. New York, USA: IEEE Publishing.

Nguyen, T. H., & Zucker, J. D. (2019). Enhancing metagenome-based disease prediction by unsupervised binning approaches. Paper presented at The 11th International Conference on Knowledge and Systems Engineering, Da Nang, Vietnam.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(85), 2825-2830.

Pasolli, E., Truong, D. T., Malik, F., Waldron, L., & Segata, N. (2016). Machine learning meta-analysis of large metagenomic datasets: tools and biological insights. PLOS Computational Biology, 12(7), 1-26.

Pasolli, E., Schiffer, L., Manghi, P., Renson, A., Obenchain, A., Truong, D. T., … Waldron, L. (2017). Accessible, curated metagenomic data through ExperimentHub. Natural Methods, 14, 1023-1024.

Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., … Wang, J. (2010). A human gut microbial gene catalogue established by metagenomic sequencing. Nature, 464, 59-65.

Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., … Wang, J. (2012). A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature, 490, 55-60.

Qin, N., Yang, F., Li, A., Prifti, E., Chen, Y., Shao, L., … Li, L. (2014). Alterations of the human gut microbiome in liver cirrhosis. Nature, 513, 59-64.

Rakel, D., & Rakel, R. E. (2011). Textbook of Family Medicine. Pennsylvania, USA: Saunders Publishing.

Reiman, D., Metwally, A., & Dai, Y. (2017), Using convolutional neural networks to explore the microbiome, Engineering in Medicine and Biology Society (EMBC). Paper presented at The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Seogwipo, South Korea.

Reiman, D., Metwally, A. A., & Dai, Y. (2018). PopPhy-CNN: A phylogenetic tree embedded architecture for convolution neural networks for metagenomic data. Oxford, UK: Oxford University Express Publishing.

Rivera-Pinto, J., Egozcue, J. J., Pawlowsky-Glahn, V., Paredes, R., Noguera-Julian, M., & Calle, M. L. (2018). Balances: a New Perspective for Micro- biome Analysis. mSystems, 3(4), 1-12.

Saitta, L. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.

Statnikov, A., Henaff, M., Narendra, V., Konganti, K., Li, Z., Yang, L., … Alekseyenko, A. V. (2013). A comprehensive evaluation of multicategory classification methods for microbiomic data. Microbiome, 1(1), 1-12.

Svozil, D., Kvasnicka, V. & Pospichal, J.(1997). Introduction to multi-layer feed-forward neural networks. Chemometrics and intelligent laboratory systems. 39(1), 43-62.

The Academy of Medical Sciences. (2015). Stratified, personalised or P4 medicine: a new direction for placing the patient at the centre of healthcare and health education. Retrieved from https://acmedsci.ac.uk/viewFile/564091e072d41.pdf.

Umeo, H., Kamikawa, N., Nishioka, K., & Akiguchi, S. (2009). Simulation of generalized synchronization processes on one-dimensional cellular automata. In R. Imre, M. Demiralp, & N. Mastorakis (Eds.), Proceedings of the 9th WSEAS International Conference on Simulation, Modelling and Optimization (pp. 350-357). Wiscosin, USA: World Scientific and Engineering Academy and Society (WSEAS) Publishing.

Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P. A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research, 11, 3371-3408.

Virgin, H. W., & Todd, J. A. (2011). Metagenomics and personalized medicine. Cell, 147(1), 44-56.

Wassan, J. T., Wang, H., Browne, F., & Zheng, H. (2018). A Comprehensive Study on Predicting Functional Role of Metagenomes Using Machine Learning Methods. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(3), 751-763.

Zeller, G., Tap, J., Voigt, A. Y., Sunagawa, S., Kultima, J. R., Costea, P. I., … Bork, P. (2014). Potential of fecal microbiota for early‐stage detection of colorectal cance. Molecular Systems Biology, 10(11), 1-18.

Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67, 301-320.



Volume and Issues


Natural Sciences and Technology

How to Cite

Tài, P. T., Phúc, T. Đặng V., Thảo, P. N. M., Chăm, N. T. N., Tính, Đào C., Ngọc, P. H., & Hải, N. T. (2020). EVALUATION OF ASSISTANCE TOOLS FOR DIAGNOSIS OF DISEASES BY APPROACHING TO PERSONALIZED MEDICINE ON METAGENOMIC DATA. Dalat University Journal of Science, 10(2), 117-144. https://doi.org/10.37569/DalatUniversity.10.2.646(2020)