ArticleMetabolites2022
A Comprehensive Evaluation of Metabolomics Data Preprocessing Methods for Deep Learning.
Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed, 19 citations in OpenAlex.
- Multi-omic modelling of body mass index response to a dietary weight loss intervention.Gut microbes · 2026Article
- Article
- Mass spectrometry-based metabolomics approaches to interrogate host-microbiome interactions in mammalian systems.Natural product reports · 2026Review
- ML-BUSMetab: Machine Learning-Based Metabolomic Profiling for Predicting Aspirin Response in Colorectal Cancer Chemoprevention: A Multi-Model Explainable Artificial Intelligence Approach with External Validation.Journal of clinical medicine · 2026Article
- Information-Content-Informed Kendall-Tau Correlation Methodology: Interpreting Missing Values in Metabolomics as Potentially Useful Information.Metabolites · 2026Article
- Decoding plant physiology through systems biology: Integrative multi-omics and computational perspectives for next-generation crop design.Plant communications · 2026Review
- Untargeted metabolomics analysis of serum metabolic signatures as novel biomarkers for gastric carcinoma.World journal of clinical oncology · 2025Article
- Artificial intelligence in the management of metabolic disorders: a comprehensive review.Journal of endocrinological investigation · 2025Review
- Distinct perturbances in metabolic pathways associate with disease progression in inflammatory bowel disease.Journal of Crohn's & colitis · 2025Observational
- Uncertainty Quantification and Flagging of Unreliable Predictions in Predicting Mass Spectrometry-Related Properties of Small Molecules Using Machine Learning.International journal of molecular sciences · 2024Article
- An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.Nature communications · 2024Article
- Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism.Current opinion in chemical biology · 2023Review
- Applications of machine learning in metabolomics: Disease modeling and classification.Frontiers in genetics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Machine learning has greatly advanced over the past decade, owing to advances in algorithmic innovations, hardware acceleration, and benchmark datasets to train on domains such as computer vision, natural-language processing, and more recently the life sciences. In particular, the subfield of machine learning known as deep learning has found applications in genomics, proteomics, and metabolomics. However, a thorough assessment of how the data preprocessing methods required for the analysis of life science data affect the performance of deep learning is lacking. This work contributes to filling that gap by assessing the impact of commonly used as well as newly developed methods employed in data preprocessing workflows for metabolomics that span from raw data to processed data. The results from these analyses are summarized into a set of best practices that can be used by researchers as a starting point for downstream classification and reconstruction tasks using deep learning.
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.