Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

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

:
Volltext urn:nbn:de:0011-n-5249900 (1.5 MByte PDF)
MD5 Fingerprint: 8f483937a42d1d0ad3c3dfca5794fb1a
(CC) by
Erstellt am: 18.12.2018


BMC systems biology. Online journal 12 (2018), Art. 60, 23 S.
http://www.biomedcentral.com/1752-0509/
ISSN: 1752-0509
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer ITEM ()
molecular signature; Omics data; stratification; systems medicine

Abstract
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.

: http://publica.fraunhofer.de/dokumente/N-524990.html