ArticleBMC systems biology2018
A computational framework for complex disease stratification from multiple large-scale datasets.
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Elucidating molecular mechanisms of allergic sensitization to olive pollen through transcriptomics.Frontiers in immunology · 2026Article
- Radiomultiomics: quantitative CT clusters of severe asthma associated with multiomics.The European respiratory journal · 2024Article
- Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop.The Journal of allergy and clinical immunology · 2024Article
- Low levels of endogenous anabolic androgenic steroids in females with severe asthma taking corticosteroids.ERJ open research · 2023Article
- Topological data analysis identifies molecular phenotypes of idiopathic pulmonary fibrosis.Thorax · 2023Article
- Eosinophil granule proteins as a biomarker in managing asthma and allergies.Asia Pacific allergy · 2023Review
- Systems Biology in Asthma.Advances in experimental medicine and biology · 2023Article
- Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19.Frontiers in immunology · 2022Article
- COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.Molecular systems biology · 2021Article
- Biomarkers for diagnosis and prediction of therapy responses in allergic diseases and asthma.Allergy · 2020Review
- The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health.Frontiers in big data · 2020Article
- Needs for Systems Approaches to Better Treat Individuals With Severe Asthma: Predicting Phenotypes and Responses to Treatments.Frontiers in medicine · 2020Review
- Review
Corrections and comments
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Authors and funding
33 authors.
Funding
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Abstract
backgroundMultilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states.
methodsThe framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification.
resultsWe illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes.
conclusionsThis framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.
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