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• W3089891425 abstract "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." @default.
• W3089891425 created "2020-10-08" @default.
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• W3089891425 date "2020-01-01" @default.
• W3089891425 modified "2023-10-10" @default.
• W3089891425 title "Deep metabolome: Applications of deep learning in metabolomics" @default.
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• W3089891425 doi "https://doi.org/10.1016/j.csbj.2020.09.033" @default.
• W3089891425 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7575644" @default.
• W3089891425 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33133423" @default.
• W3089891425 hasPublicationYear "2020" @default.
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