Publication: Deep metabolome: Applications of deep learning in metabolomics
Issued Date
2020-01-01
Resource Type
ISSN
20010370
Other identifier(s)
2-s2.0-85092695235
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Mahidol University
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SCOPUS
Bibliographic Citation
Computational and Structural Biotechnology Journal. Vol.18, (2020), 2818-2825
Suggested Citation
Yotsawat Pomyen, Kwanjeera Wanichthanarak, Patcha Poungsombat, Johannes Fahrmann, Dmitry Grapov, Sakda Khoomrung Deep metabolome: Applications of deep learning in metabolomics. Computational and Structural Biotechnology Journal. Vol.18, (2020), 2818-2825. doi:10.1016/j.csbj.2020.09.033 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/59900
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Title
Deep metabolome: Applications of deep learning in metabolomics
Abstract
© 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.