ArticlePLoS computational biology2018
An evolutionary learning and network approach to identifying key metabolites for osteoarthritis.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Pooled it
- Acute-phase serum metabolomics signatures for predicting post-stroke cognitive impairment after ischemic stroke: a prospective cohort study.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Precision Medicine Approaches with Metabolomics and Artificial Intelligence.International journal of molecular sciences · 2022Review
- Interpretable machine learning methods for predictions in systems biology from omics data.Frontiers in molecular biosciences · 2022Review
- The role of metabolomics in precision medicine of osteoarthritis: How far are we?Osteoarthritis and cartilage open · 2021Article
- SMILE: systems metabolomics using interpretable learning and evolution.BMC bioinformatics · 2021Article
- Machine Learning in Rheumatic Diseases.Clinical reviews in allergy & immunology · 2021Review
- Serum fatty acid chain length associates with prevalent symptomatic end-stage osteoarthritis, independent of BMI.Scientific reports · 2020Observational
- Article
- Differential correlation network analysis identified novel metabolomics signatures for non-responders to total joint replacement in primary osteoarthritis patients.Metabolomics : Official journal of the Metabolomic Society · 2020Article
- Review
- Article
- Article
- Dissecting the Genetics of Osteoporosis using Systems Approaches.Trends in genetics : TIG · 2019Review
- Phenotypes of osteoarthritis: current state and future implications.Clinical and experimental rheumatologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolomics studies use quantitative analyses of metabolites from body fluids or tissues in order to investigate a sequence of cellular processes and biological systems in response to genetic and environmental influences. This promises an immense potential for a better understanding of the pathogenesis of complex diseases. Most conventional metabolomics analysis methods exam one metabolite at a time and may overlook the synergistic effect of combining multiple metabolites. In this article, we proposed a new bioinformatics framework that infers the non-linear synergy among multiple metabolites using a symbolic model and subsequently, identify key metabolites using network analysis. Such a symbolic model is able to represent a complex non-linear relationship among a set of metabolites associated with osteoarthritis (OA) and is automatically learned using an evolutionary algorithm. Applied to the Newfoundland Osteoarthritis Study (NFOAS) dataset, our methodology was able to identify nine key metabolites including some known osteoarthritis-associated metabolites and some novel metabolic markers that have never been reported before. The results demonstrate the effectiveness of our methodology and more importantly, with further investigations, propose new hypotheses that can help better understand the OA disease.
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.