TY - JOUR
T1 - A computational framework for complex disease stratification from multiple large-scale datasets
AU - the U-BIOPRED Study Group and the eTRIKS Consortium
AU - de Meulder, Bertrand
AU - Lefaudeux, Diane
AU - Bansal, Aruna T.
AU - Mazein, Alexander
AU - Chaiboonchoe, Amphun
AU - Ahmed, Hassan
AU - Balaur, Irina
AU - Saqi, Mansoor
AU - Pellet, Johann
AU - Ballereau, Stéphane
AU - Lemonnier, Nathanaël
AU - Sun, Kai
AU - Pandis, Ioannis
AU - Yang, Xian
AU - Batuwitage, Manohara
AU - Kretsos, Kosmas
AU - van Eyll, Jonathan
AU - Bedding, Alun
AU - Davison, Timothy
AU - Dodson, Paul
AU - Larminie, Christopher
AU - Postle, Anthony
AU - Corfield, Julie
AU - Djukanovic, Ratko
AU - Chung, Kian Fan
AU - Adcock, Ian M.
AU - Guo, Yi-Ke
AU - Sterk, Peter J.
AU - Manta, Alexander
AU - Rowe, Anthony
AU - Baribaud, Frédéric
AU - Auffray, Charles
AU - Gibeon, David
AU - Hoda, Uruj
AU - Kuo, Scott
AU - Meah, Sally
AU - Meiser, Andrea
AU - Fleming, Louise J.
AU - Hu, Sile
AU - Pavlidis, Stelios
AU - Rossios, Christos
AU - Russel, Kirsty
AU - Bel, Elisabeth
AU - Brinkman, Paul
AU - Dijkhuis, Annemiek
AU - Hashimoto, Simone
AU - Lutter, Rene
AU - van Aalderen, Wim
AU - van Drunen, Kees
AU - Zwinderman, Koos
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85047816768&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/29843806
U2 - https://doi.org/10.1186/s12918-018-0556-z
DO - https://doi.org/10.1186/s12918-018-0556-z
M3 - Article
C2 - 29843806
SN - 1752-0509
VL - 12
JO - BMC systems biology
JF - BMC systems biology
IS - 1
M1 - 60
ER -