Deep metabolome: Applications of deep learning in metabolomics

Yotsawat Pomyen; Kwanjeera Wanichthanarak; Patcha Poungsombat; Johannes Fahrmann; Dmitry Grapov; Sakda Khoomrung

doi:10.1016/j.csbj.2020.09.033

Deep metabolome: Applications of deep learning in metabolomics

Comput Struct Biotechnol J. 2020 Oct 1:18:2818-2825. doi: 10.1016/j.csbj.2020.09.033. eCollection 2020.

Authors

Yotsawat Pomyen¹, Kwanjeera Wanichthanarak^{2

3}, Patcha Poungsombat^{2

3

4}, Johannes Fahrmann⁵, Dmitry Grapov⁶, Sakda Khoomrung^{2

3

4}

Affiliations

¹ Translational Research Unit, Chulabhorn Research Institute, Bangkok, Thailand.
² Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.
³ Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.
⁴ Center for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand.
⁵ Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.
⁶ CDS- Creative Data Solutions LLC, https://creative-data.solutions, USA.

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.

Keywords: AI, Artificial Intelligence; ANN, Artificial Neural Network; AUC, Area Under the receiver-operating characteristic Curve; Artificial neural network; CCS value, Collision Cross Section value; CFM-EI, Competitive Fragmentation Modeling-Electron Ionization; CNN, Convolutional Neural Network; DL, Deep Learning; DNN, Deep Neural Network; Deep learning; 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; LC-MS, Liquid Chromatography-Mass Spectrometry; LSTM, Long Short-Term Memory; ML, Machine Learning; MLP, Multi-layered Perceptron; MS, Mass Spectrometry; Mass spectrometry; Metabolomics; NEIMS, Neural Electron-Ionization Mass Spectrometry; NMR; NMR, Nuclear Magnetic Resonance; NUS, Non-Uniformly Sampling; PARAFAC2, Parallel Factor Analysis 2; RF, Random Forest; RNN, Recurrent Neural Network; ReLU, Rectified Linear Unit; 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; istHMS, Implementation of IST at Harvard Medical School; m/z, mass/charge ratio.

Publication types

Review