ArticleNature communications2021
Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses 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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Artificial intelligence at the gut-oral microbiota frontier: mapping machine learning tools for gastric cancer risk prediction.Biomedical engineering online · 2025Pooled it
- Maternal gut microbiota in the postpartum Period: A Systematic review.European journal of obstetrics, gynecology, and reproductive biology · 2023Pooled it
- Anxiety associated with dietary intake and gut microbiome features in a cross-sectional cohort of sub-clinically anxious young women.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Article
- Explainable machine learning model for classifying atherosclerotic cardiovascular disease in patients with metabolic dysfunction-associated steatotic liver disease.Frontiers in endocrinology · 2025Article
- Neuroimmune dynamics and brain aging: mechanisms and consequences.Frontiers in aging neuroscience · 2025Review
- Simplicity within biological complexity.Bioinformatics advances · 2025Article
- The Influence of Gastric Microbiota and Probiotics inBiomedicines · 2024Review
- Construction and optimization of non-parametric analysis model for meter coefficients via back propagation neural network.Scientific reports · 2024Article
- Uncovering Predictive Factors and Interventions for Restoring Microecological Diversity after Antibiotic Disturbance.Nutrients · 2023Article
- Intelligent Biological Networks: Improving Anti-Microbial Resistance Resilience through Nutritional Interventions to Understand Protozoal Gut Infections.Microorganisms · 2023Review
- The role of microbiomes in gastrointestinal cancers: new insights.Frontiers in oncology · 2023Review
- Geometrical congruence, greedy navigability and myopic transfer in complex networks and brain connectomes.Nature communications · 2022Article
- Beyond Basic Diversity Estimates-Analytical Tools for Mechanistic Interpretations of Amplicon Sequencing Data.Microorganisms · 2022Review
- Identifying cellular cancer mechanisms through pathway-driven data integration.Bioinformatics (Oxford, England) · 2022Article
- Microbial metabolites in the marine carbon cycle.Nature microbiology · 2022Review
- Article
- Progress in elucidating the relationship betweenWorld journal of gastroenterology · 2021Article
- Age-sex population adjusted analysis of disease severity in epidemics as a tool to devise public health policies for COVID-19.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 6 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in early phase and rely principally on linear techniques for multivariate analysis. Here we disclose the importance of complementing linear dimensionality reduction techniques with nonlinear ones to unveil hidden patterns that remain unseen by linear embedding. Then, we prove the advantages to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of bacterial network re-organizations which emerge from perturbations induced by a medical treatment (PPIs) or an infectious state (H. pylori). Finally, we show how to build bacteria-metabolite multilayer networks that can deepen our understanding of the metabolite pathways significantly associated to the perturbed microbial communities.
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.