期刊名称：Computational and Structural Biotechnology Journal
印刷版ISSN：2001-0370
出版年度：2020
卷号：18
页码：2818-2825
DOI：10.1016/j.csbj.2020.09.033
出版社：Computational and Structural Biotechnology Journal
摘要：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.
关键词：Metabolomics ; NMR ; Mass spectrometry ; Artificial neural network ; Deep learning ; AI Artificial Intelligence ; ANN Artificial Neural Network ; AUC Area Under the receiver-operating characteristic Curve ; CFM-EI Competitive Fragmentation Modeling-Electron Ionization ; CNN Convolutional Neural Network ; CCS value Collision Cross Section value ; DL Deep Learning ; DNN Deep Neural Network ; ECFP Extended Circular Fingerprint ; ER Estrogen Receptor ; FID Free Induction Decay ; FP score Fingerprint correlation score ; FTIR Fourier Transform Infrared ; GC–MS Gas Chromatography-Mass Spectrometry ; HDLSS data High Dimensional Low Sample Size data ; IST Iterative Soft Thresholding ; istHMS Implementation of IST at Harvard Medical School ; LC-MS Liquid Chromatography-Mass Spectrometry ; LSTM Long Short-Term Memory ; ML Machine Learning ; MLP Multi-layered Perceptron ; MS Mass Spectrometry ; m ;z mass/charge ratio ; NEIMS Neural Electron-Ionization Mass Spectrometry ; NMR Nuclear Magnetic Resonance ; NUS Non-Uniformly Sampling ; PARAFAC2 Parallel Factor Analysis 2 ; ReLU Rectified Linear Unit ; RF Random Forest ; RNN Recurrent Neural Network ; SMARTS SMILES arbitrary target specification ; SMILE Sparse Multidimensional Iterative Lineshape-enhanced ; SMILES Simplified Molecular-Input Line-Entry System ; SRA Sequence Read Archive ; VAE Variational Autoencoder
