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

: Meulder, Bertrand de; Lefaudeux, Diane; Bansal, Aruna T.; Mazein, Alexander; Chaiboonchoe, Amphun; Ahmed, Hassan; Balaur, Irina; Saqi, Mansoor; Pellet, Johann; Ballereau, Stephane; Lemonnier, Nathanaël; Sun, Kai; Pandis, Ioannis; Yang, Xian; Batuwitage, Manohara; Kretsos, Kosmas; Eyll, Jonathan van; Bedding, Alun; Davison, Timothy; Dodson, Paul; Larminie, Christopher; Postle, Anthony; Corfield, Julie; Djukanović, Ratko; Chung, Kian Fan; Adcock, Ian M.; Guo, Yi-Ke; Sterk, Peter J.; Manta, Alexander; Rowe, Anthony; Baribaud, Frédéric; Auffray, Charles; Badorrek, Philipp; Faulenbach, Cornelia; Braun, Armin; Hohlfeld, Jens; Krug, Norbert

Fulltext urn:nbn:de:0011-n-5249900 (1.5 MByte PDF)
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Created on: 18.12.2018

BMC systems biology. Online journal 12 (2018), Art. 60, 23 pp.
ISSN: 1752-0509
Journal Article, Electronic Publication
Fraunhofer ITEM ()
molecular signature; Omics data; stratification; systems medicine

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.