ReviewComputational and structural biotechnology journal2020
Deep metabolome: Applications of deep learning in metabolomics.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
79 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A bibliometric and text-mining analysis of lipidomics and metabolomics in human disease.Frontiers in physiology · 2026Pooled it
- Machine learning and data-driven inverse modeling of metabolomics unveil key processes of active aging.NPJ systems biology and applications · 2025Trial
- Lipidomics study of plasma from patients suggest that ALS and PLS are part of a continuum of motor neuron disorders.Scientific reports · 2021Trial
- Multimodal Transformer Fusion of PTR-ToF-MS Volatiles and Targeted Non-Volatile Metabolites Enables Data-Driven Grading of Baimudan White Tea.Rapid communications in mass spectrometry : RCM · 2026Article
- Process Analytical Technology Integration in 3D Printing Processes: Benefits and Challenges.AAPS PharmSciTech · 2026Review
- Gas Chromatography Chemometrics of Volatile Analytes in Mexican Mezcals: Fingerprinting Ancestral and Artisanal Manufacturing Processes, Geographical Origins, and Agave Species.Foods (Basel, Switzerland) · 2026Article
- Will predictive AI make discovery research in metabolism obsolete.npj metabolic health and disease · 2026Article
- Cultivar to chemotype: characterizing complex botanicals with mass spectrometry metabolomics.Natural product reports · 2026Review
- Metabolomics and Artificial Intelligence (AI) assisted metabolomics in the diagnosis of non-tuberculous mycobacterial infections: progress and challenges.New microbes and new infections · 2026Review
- Simulation of the Metabolic Response to an Interventional Study with New Healthy Beverages by Machine-Learning Regression.Journal of agricultural and food chemistry · 2026Article
- LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening.Analytical chemistry · 2026Article
- Early Cancer Detection: What's Going on and What's Next.MedComm · 2026Review
- Prediction of influenza virus infection based on deep learning and peripheral blood proteomics: A diagnostic study.Journal of advanced research · 2026Article
- Metabolomics-Driven Precision Prevention and Treatment of Heart Failure with Traditional Chinese Medicine: From Mechanistic Insights to Metabolic Network Regulation.Drug design, development and therapy · 2026Review
- Article
- Review
- Recent strategies and methodological advances for microbial natural product research in the post-genomics era.Archives of microbiology · 2025Review
- Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.Chemical reviews · 2025Review
- The human placenta and its role in reproductive outcomes revisited.Physiological reviews · 2025Review
- Endocrine regulation of metabolic crosstalk between liver and brown adipose tissue by natural active ingredients.International journal of obesity (2005) · 2025Review
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.