ArticleMetabolites2013
Integrative analysis of longitudinal metabolomics data from a personal multi-omics profile.
Article in Metabolites, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed.
- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- Genome-wide characteristics and potential functions of circular RNAs from the embryo muscle development in Chengkou mountain chicken.Frontiers in veterinary science · 2024Article
- Multiomics Analysis of Plasma Proteomics and Metabolomics of Steroid Resistance in Childhood Nephrotic Syndrome Using a "Patient-Specific" Approach.Kidney international reports · 2023Article
- Multi-Omics Profiling for Health.Molecular & cellular proteomics : MCP · 2023Review
- Big Data in Gastroenterology Research.International journal of molecular sciences · 2023Review
- Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension.Circulation research · 2022Review
- Characterization of lncRNA/circRNA-miRNA-mRNA network to reveal potential functional ceRNAs in the skeletal muscle of chicken.Frontiers in physiology · 2022Article
- Visibility graph based temporal community detection with applications in biological time series.Scientific reports · 2021Article
- Old and New Biomarkers Associated with Endothelial Dysfunction in Chronic Hyperglycemia.Oxidative medicine and cellular longevity · 2021Review
- A Customizable Analysis Flow in Integrative Multi-Omics.Biomolecules · 2020Review
- Role of Polyphenols and Carotenoids in Endothelial Dysfunction: An Overview from Classic to Innovative Biomarkers.Oxidative medicine and cellular longevity · 2020Review
- The Need for Multi-Omics Biomarker Signatures in Precision Medicine.International journal of molecular sciences · 2019Review
- A computational framework for complex disease stratification from multiple large-scale datasets.BMC systems biology · 2018Article
- Whole-transcriptome analysis of atrophic ovaries in broody chickens reveals regulatory pathways associated with proliferation and apoptosis.Scientific reports · 2018Article
- From systems biology to P4 medicine: applications in respiratory medicine.European respiratory review : an official journal of the European Respiratory Society · 2018Review
- Consensus Clustering of temporal profiles for the identification of metabolic markers of pre-diabetes in childhood (EarlyBird 73).Scientific reports · 2018Article
- Personalised Interventions-A Precision Approach for the Next Generation of Dietary Intervention Studies.Nutrients · 2017Review
- -Omic and Electronic Health Record Big Data Analytics for Precision Medicine.IEEE transactions on bio-medical engineering · 2017Review
- A Systems Medicine Approach: Translating Emerging Science into Individualized Wellness.Advances in medicine · 2017Review
- Modelling idiopathic Parkinson disease as a complex illness can inform incidence rate in healthy adults: the PThe European journal of neuroscience · 2017Article
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
Abstract
The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways' differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling.
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.