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

Title: A computational framework for complex disease stratification from multiple large-scale datasets
Authors: De Meulder, B
Lefaudeux, D
Bansal, AT
Mazein, A
Chaiboonchoe, A
Ahmed, H
Balaur, I
Saqi, M
Pellet, J
Ballereau, S
Lemonnier, N
Sun, K
Pandis, I
Yang, X
Batuwitage, M
Kretsos, K
Van Eyll, J
Bedding, A
Davison, T
Dodson, P
Larminie, C
Postle, A
Corfield, J
Djukanovic, R
Chung, KF
Adcock, IM
Guo, Y-K
Sterk, PJ
Manta, A
Rowe, A
Baribaud, F
Auffray, C
U-BIOPRED Study Group and the eTRIKS Consortium
Item Type: Journal Article
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.
Issue Date: 29-May-2018
Date of Acceptance: 21-Feb-2018
URI: http://hdl.handle.net/10044/1/60119
DOI: https://dx.doi.org/10.1186/s12918-018-0556-z
ISSN: 1752-0509
Publisher: BioMed Central
Journal / Book Title: BMC Systems Biology
Volume: 12
Issue: 1
Copyright Statement: © The Author(s). 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 115010
Keywords: Molecular signatures
Systems medicine
‘Omics data
U-BIOPRED Study Group and the eTRIKS Consortium
1199 Other Medical And Health Sciences
Publication Status: Published
Conference Place: England
Article Number: ARTN 60
Appears in Collections:Computing
National Heart and Lung Institute
Faculty of Engineering