Yotsawat PomyenKwanjeera WanichthanarakPatcha PoungsombatJohannes FahrmannDmitry GrapovSakda KhoomrungChulabhorn Research InstituteUniversity of Texas MD Anderson Cancer CenterMahidol UniversityFaculty of Medicine, Siriraj Hospital, Mahidol UniversityCDS- Creative Data Solutions LLC2020-11-182020-11-182020-01-01Computational and Structural Biotechnology Journal. Vol.18, (2020), 2818-2825200103702-s2.0-85092695235https://repository.li.mahidol.ac.th/handle/20.500.14594/59900© 2020 The Author(s) In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learning can be effectively applied to metabolomics, several challenges should be addressed, including metabolome-specific deep learning architectures, dimensionality problems, and model evaluation regimes.Mahidol UniversityBiochemistry, Genetics and Molecular BiologyComputer ScienceReviewSCOPUS10.1016/j.csbj.2020.09.033