Deep metabolome: Applications of deep learning in metabolomics

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

doi:10.1016/j.csbj.2020.09.033

Cited by 110 publications

(67 citation statements)

References 84 publications

(100 reference statements)

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“…Thus, as second approach, we used random forest (no. of trees = 5000), a machine learning approach 20 to select the best performing lipid species per pairwise comparison, based on the lowest mean values for minimum depths in the trees (lower the better) and the frequencies found in trees (higher the better). Minimum depth indicates how early in decision trees a lipid species is involved (Fig.…”

Section: Resultsmentioning

confidence: 99%

Lipidomics study of plasma from patients suggest that ALS and PLS are part of a continuum of motor neuron disorders

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Yun

et al. 2021

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Motor neuron disorders (MND) include a group of pathologies that affect upper and/or lower motor neurons. Among them, amyotrophic lateral sclerosis (ALS) is characterized by progressive muscle weakness, with fatal outcomes only in a few years after diagnosis. On the other hand, primary lateral sclerosis (PLS), a more benign form of MND that only affects upper motor neurons, results in life-long progressive motor dysfunction. Although the outcomes are quite different, ALS and PLS present with similar symptoms at disease onset, to the degree that both disorders could be considered part of a continuum. These similarities and the lack of reliable biomarkers often result in delays in accurate diagnosis and/or treatment. In the nervous system, lipids exert a wide variety of functions, including roles in cell structure, synaptic transmission, and multiple metabolic processes. Thus, the study of the absolute and relative concentrations of a subset of lipids in human pathology can shed light into these cellular processes and unravel alterations in one or more pathways. In here, we report the lipid composition of longitudinal plasma samples from ALS and PLS patients initially, and after 2 years following enrollment in a clinical study. Our analysis revealed common aspects of these pathologies suggesting that, from the lipidomics point of view, PLS and ALS behave as part of a continuum of motor neuron disorders.

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Section: Resultsmentioning

confidence: 99%

Lipidomics study of plasma from patients suggest that ALS and PLS are part of a continuum of motor neuron disorders

Area‐Gómez

Larrea

Yun

et al. 2021

Sci Rep

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“…Despite the considerable efforts of the aforementioned ML methods to rapidly and accurately characterize non-linear complex samples and their metabolite properties, false positive signals, co-eluting metabolites and retention time shifts are still major bottlenecks that effect data analysis and interpretation of plant MS-based metabolomics. Artificial neural networks (ANNs) and deep learning (DL) methods are proposed to solve these issues and other bottlenecks involved in the mining of metabolomics data [ 154 , 197 ].…”

Section: 4ir Technologies and Plant Metabolomicsmentioning

confidence: 99%

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

et al. 2021

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Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

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“…Artificial neural networks are ML tools based on interconnecting hidden layers, computational structures inspired by neurons in the brain, and in their simplest forms are similar to PLS but can model nonlinear models. Deep neuronal networks, or DL, can predict relationships from diverse datasets and can accomplish supervised, semisupervised and unsupervised tasks, improving the interpretability of data analysis [ 140 , 141 , 142 , 143 ]. DL techniques transform the data by iteratively tuning their internal parameters and may enable the extraction of the most predictive features from complex datasets.…”

Section: Machine Intelligence and Learning Approachesmentioning

confidence: 99%

Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview

et al. 2021

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Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.

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Deep metabolome: Applications of deep learning in metabolomics

Abstract: Graphical abstract

Cited by 110 publications

References 84 publications

Lipidomics study of plasma from patients suggest that ALS and PLS are part of a continuum of motor neuron disorders

Lipidomics study of plasma from patients suggest that ALS and PLS are part of a continuum of motor neuron disorders

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview

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