ArticleJournal of clinical bioinformatics2011
Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers.
Article in Journal of clinical bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Metabolomics in Exercise and Sports: A Systematic Review.Sports medicine (Auckland, N.Z.) · 2022Pooled it
- Novel ratio-expressions of genes enables estimation of wound age in contused skeletal muscle.International journal of legal medicine · 2024Article
- Human disease biomarker panels through systems biology.Biophysical reviews · 2021Review
- Prognosis prediction of hepatocellular carcinoma after surgical resection based on serum metabolic profiling from gas chromatography-mass spectrometry.Analytical and bioanalytical chemistry · 2021Article
- Article
- Identification of Prognostic Biomarkers for Multiple Solid Tumors Using a Human Villi Development Model.Frontiers in cell and developmental biology · 2020Article
- A novel network-based approach for discovering dynamic metabolic biomarkers in cardiovascular disease.PloS one · 2018Article
- A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks.Scientific reports · 2017Article
- A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma.Scientific reports · 2016Article
- The Healthy Core Metabolism: A New Paradigm for Primary Preventive Nutrition.The journal of nutrition, health & aging · 2016Article
- Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity.PLoS computational biology · 2015Article
- Metabolomics in the developmental origins of obesity and its cardiometabolic consequences.Journal of developmental origins of health and disease · 2015Review
- Analytical methods in untargeted metabolomics: state of the art in 2015.Frontiers in bioengineering and biotechnology · 2015Review
- Aging-related Changes in Mouse Serum Glycerophospholipid Profiles.Osong public health and research perspectives · 2014Article
- PECA: a novel statistical tool for deconvoluting time-dependent gene expression regulation.Journal of proteome research · 2014Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn metabolomics, biomarker discovery is a highly data driven process and requires sophisticated computational methods for the search and prioritization of novel and unforeseen biomarkers in data, typically gathered in preclinical or clinical studies. In particular, the discovery of biomarker candidates from longitudinal cohort studies is crucial for kinetic analysis to better understand complex metabolic processes in the organism during physical activity.
findingsIn this work we introduce a novel computational strategy that allows to identify and study kinetic changes of putative biomarkers using targeted MS/MS profiling data from time series cohort studies or other cross-over designs. We propose a prioritization model with the objective of classifying biomarker candidates according to their discriminatory ability and couple this discovery step with a novel network-based approach to visualize, review and interpret key metabolites and their dynamic interactions within the network. The application of our method on longitudinal stress test data revealed a panel of metabolic signatures, i.e., lactate, alanine, glycine and the short-chain fatty acids C2 and C3 in trained and physically fit persons during bicycle exercise.
conclusionsWe propose a new computational method for the discovery of new signatures in dynamic metabolic profiling data which revealed known and unexpected candidate biomarkers in physical activity. Many of them could be verified and confirmed by literature. Our computational approach is freely available as R package termed BiomarkeR under LGPL via CRAN http://cran.r-project.org/web/packages/BiomarkeR/.
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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.