Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
4.1
Journals
Metabolites
Special Issues
Deep Learning for Metabolomics
share announcement

Deep Learning for Metabolomics

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 6770

Special Issue Editors

Dr. Alexandre Perera-LLuna

E-Mail Website
Guest Editor
Centre de Recerca en Enginyeria Biomèdica, Universitat Politècnica de Catalunya, Pau Gargallo 5, 08028 Barcelona, Spain
Interests: bioinformatics; bioengineering; signal processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Sangkyu Park

E-Mail Website
Guest Editor
FACE (Food webs, Arctic, and Chemical Ecology) Laboratory, Department of Biological Science, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon 16499, Kyeonggi-do, Korea
Interests: food webs; energy flow; plant metabolomics; deep learning; chemical ecology

Special Issue Information

Dear Colleagues,

Artificial intelligence methods are rapidly advancing in the viral conquests of scientific disciplines. Analytical chemistry and, in particular, metabolomics have not been impervious to this wave, due not only to the advent of novel mathematical approaches allowing for a richer analysis of complex data, but also to the availability of larger datasets allowing the training of large parametric models such as deep neural networks through deep learning (DL).

 

The opportunities for DL in the field of metabolomics are numerous. These methods could potentially help us to better understand the chemical structure of metabolomic data such as the identification or classification of compounds; create new encodings of the chemical space; build better predictors for biological samples; classify their physiological, ecological, or taxonomical statuses; provide enhanced quality control methods through better learning and modelling regarding instrumental errors; and integrate other omics through hybrid network configurations or even step-on system biology network-based representations. 

The current Special Issue invites papers on all aspects of metabolomics research, with an emphasis on deep learning workflows applied for the analysis or the understanding of metabolomic datasets. All methodological approaches employing deep configurations to all steps of metabolomics workflows including the creation of synthetic data are welcome.

Dr. Alexandre Perera-LLuna
Dr. Sangkyu Park
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • metabolomics
  • autoencoders
  • vairational autoencoders
  • deep neural networks
  • synthetic data generation

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

15 pages, 2442 KiB  
Article
Serum Metabolites Associated with Blood Pressure in Chronic Kidney Disease Patients
by Fengyao Yan, Dan-Qian Chen, Jijun Tang, Ying-Yong Zhao and Yan Guo
Metabolites 2022, 12(4), 281; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12040281 - 23 Mar 2022
Cited by 2 | Viewed by 1996
Abstract
Blood pressure is one of the most basic health screenings and it has a complex relationship with chronic kidney disease (CKD). Controlling blood pressure for CKD patients is crucial for curbing kidney function decline and reducing the risk of cardiovascular disease. Two independent [...] Read more.
Blood pressure is one of the most basic health screenings and it has a complex relationship with chronic kidney disease (CKD). Controlling blood pressure for CKD patients is crucial for curbing kidney function decline and reducing the risk of cardiovascular disease. Two independent CKD cohorts, including matched controls (discovery n = 824; validation n = 552), were recruited. High-throughput metabolomics was conducted with the patients’ serum samples using mass spectrometry. After controlling for CKD severity and other clinical hypertension risk factors, we identified ten metabolites that have significant associations with blood pressure. The quantitative importance of these metabolites was verified in a fully connected neural network model. Of the ten metabolites, seven have not previously been associated with blood pressure. The metabolites that had the strongest positive association with blood pressure were aspartylglycosamine (p = 4.58 × 10−5), fructose-1,6-diphosphate (p = 1.19 × 10−4) and N-Acetylserine (p = 3.27 × 10−4). Three metabolites that were negatively associated with blood pressure (phosphocreatine, p = 6.39 × 10−3; dodecanedioic acid, p = 0.01; phosphate, p = 0.04) have been reported previously to have beneficial effects on hypertension. These results suggest that intake of metabolites as supplements may help to control blood pressure in CKD patients. Full article
(This article belongs to the Special Issue Deep Learning for Metabolomics)
►▼ Show Figures

Graphical abstract

14 pages, 2400 KiB  
Article
Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model’s Classification Performance for Alzheimer’s Disease
by Min-Hee Lee, Chang-Soo Yun, Kyuseok Kim and Youngjin Lee
Metabolites 2022, 12(3), 231; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12030231 - 07 Mar 2022
Cited by 3 | Viewed by 2197
Abstract
Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely used to predict AD using a deep learning model. However, the effects of noise and blurring on 18F-FDG PET images were [...] Read more.
Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely used to predict AD using a deep learning model. However, the effects of noise and blurring on 18F-FDG PET images were not considered. The performance of a classification model trained using raw, deblurred (by the fast total variation deblurring method), or denoised (by the median modified Wiener filter) 18F-FDG PET images without or with cropping around the limbic system area using a 3D deep convolutional neural network was investigated. The classification model trained using denoised whole-brain 18F-FDG PET images achieved classification performance (0.75/0.65/0.79/0.39 for sensitivity/specificity/F1-score/Matthews correlation coefficient (MCC), respectively) higher than that with raw and deblurred 18F-FDG PET images. The classification model trained using cropped raw 18F-FDG PET images achieved higher performance (0.78/0.63/0.81/0.40 for sensitivity/specificity/F1-score/MCC) than the whole-brain 18F-FDG PET images (0.72/0.32/0.71/0.10 for sensitivity/specificity/F1-score/MCC, respectively). The 18F-FDG PET image deblurring and cropping (0.89/0.67/0.88/0.57 for sensitivity/specificity/F1-score/MCC) procedures were the most helpful for improving performance. For this model, the right middle frontal, middle temporal, insula, and hippocampus areas were the most predictive of AD using the class activation map. Our findings demonstrate that 18F-FDG PET image preprocessing and cropping improves the explainability and potential clinical applicability of deep learning models. Full article
(This article belongs to the Special Issue Deep Learning for Metabolomics)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research

9 pages, 521 KiB  
Opinion
Metabolomics in Bariatric and Metabolic Surgery Research and the Potential of Deep Learning in Bridging the Gap
by Athanasios G. Pantelis
Metabolites 2022, 12(5), 458; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12050458 - 19 May 2022
Cited by 5 | Viewed by 1881
Abstract
During the past several years, there has been a shift in terminology from bariatric surgery alone to bariatric and metabolic surgery (BMS). More than a change in name, this signifies a paradigm shift that incorporates the metabolic effects of operations performed for weight [...] Read more.
During the past several years, there has been a shift in terminology from bariatric surgery alone to bariatric and metabolic surgery (BMS). More than a change in name, this signifies a paradigm shift that incorporates the metabolic effects of operations performed for weight loss and the amelioration of related medical problems. Metabolomics is a relatively novel concept in the field of bariatrics, with some consistent changes in metabolite concentrations before and after weight loss. However, the abundance of metabolites is not easy to handle. This is where artificial intelligence, and more specifically deep learning, would aid in revealing hidden relationships and would help the clinician in the decision-making process of patient selection in an individualized way. Full article
(This article belongs to the Special Issue Deep Learning for Metabolomics)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop