A computational framework for complex disease stratification from multiple large-scale datasets

Jørgen Vestbo (Medlem af forfattergruppering), U-BIOPRED Study Group, eTRIKS Consortium

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review


Background: Multilevel 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. Methods: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. Results: We 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. Conclusions: This 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.
TidsskriftBMC Systems Biology
Udgave nummer1
Antal sider23
StatusUdgivet - 29. maj 2018
Udgivet eksterntJa


Bibliografisk note

M1 - 60


  • 'Omics data
  • Molecular signatures
  • Stratification
  • Systems medicine