# NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
## 4 Applications of Deep Learning Approaches for NMR-Based Metabolomics
### Biomedical  
Metabolomics-based NMR investigations, coupled with deep learning methods, are increasingly employed within the biomedical field. More profoundly, the use of complex DL architectures hardly allows achieving a predictive power with ranking or selection. As already discussed, DL models use several computational layers to analyze input signals and establish any eventual preferred direction for signal encoding (forward or backward). This procedure does not usually allow the interpretation of input signals in terms of the used model, making it hard to identify biomarkers in a network, where biological and DL modeling are connected (Figure 23). Today, it is still necessary to uniform assessment metric for biomedical data classification or prediction, also avoiding false negatives in disease diagnosis. Further, deep learning is a promising methodology to treat data collecting by smart wearable sensors, which is considered fundamental in epidemic prediction, disease prevention, and clinical decision making, thus allowing a significant improvement in the quality of life [124; 125]. Figure 23: The multiomics method represented connects biological (i.e., signal inhibition, signaling network and biochemical feedback) with DL modeling (backpropagation, prediction, convolution, etc. ), aiming to maximize the robustness of the approach for the identification of biochemical features referred to specific phenotypes. Figure reprinted from Ref. [124] under the terms of the Creative Commons Attribution Noncommercial License. With the aim to obtain an accurate metabolites identification from the observation of the corresponding peaks in complex mixtures, [PERSON] et al. [126] developed a convolutional neural network (CNN) model, called SMART-Miner, which is trained on 657 chemical entities collected from HMDB and BMRB databases. After training, the model is able to automatically carry out the recognition of metabolites from \({}^{1}\)H-\({}^{13}\)C HSQC NMR spectra of complex metabolite mixtures, showing higher performance in comparison with other NMR-based metabolomic tools (Figure 24). [PERSON] et al. [127], by employing ANNs on \({}^{1}\)H NMR spectra, performed a successful classification of four lung carcinoma cell lines, showing different drug-resistance patterns. The authors chose human lung carcinoma and adenocarcinoma cell lines together with specific drug-resistant daughter lines (Figure 25). The ANN architecture was constructed at first using three layers and the corresponding weights were determined by minimizing the root mean square error. Then, the authors analyzed networks with four layers, two of which are hidden. Their results show that the four-layer structure with two hidden layers provided a 100% successful classification [127]. These data are very interesting in terms of the robustness of the used approach: the cell lines were correctly classified, even though the effects were provoked by the operator and independently from the spectra chosen for training and validation (Figure 25). Figure 24: Overlay of experimental HSQC spectra from a metabolite mixture (black correlations) and the outcomes predicted by SMART-Miner (colored correlations). Figure reprinted with permission from Ref. [126]. Copyright 2021 Wiley Periodicals, Inc. Very recently, [PERSON] et al. [128] analyzed serum samples from 94 elderly patients with early stage colorectal cancer and 75 elderly patients with metastatic colorectal cancer. With the aim to separately observe each different molecular components, these authors acquired one-dimensional proton NMR spectra by using three different pulse sequences for each sample: (i) a nuclear Overhauser effect spectroscopy pulse sequence to observe molecules with both low and high molecular weight; (ii) a common spin echo mono-dimensional pulse sequence [129] to observe only lighter metabolites and (iii) a common diffusion-edited pulse sequence to observe only macromolecules [128].