BibTex format
@article{De:2018:10.1186/s12918-018-0556-z,
author = {De, Meulder B and Lefaudeux, D and Bansal, AT and Mazein, A and Chaiboonchoe, A and Ahmed, H and Balaur, I and Saqi, M and Pellet, J and Ballereau, S and Lemonnier, N and Sun, K and Pandis, I and Yang, X and Batuwitage, M and Kretsos, K and van, Eyll J and Bedding, A and Davison, T and Dodson, P and Larminie, C and Postle, A and Corfield, J and Djukanovic, R and Chung, KF and Adcock, IM and Guo, Y-K and Sterk, PJ and Manta, A and Rowe, A and Baribaud, F and Auffray, C and U-BIOPRED, Study Group and the eTRIKS Consortium},
doi = {10.1186/s12918-018-0556-z},
journal = {BMC Systems Biology},
title = {A computational framework for complex disease stratification from multiple large-scale datasets},
url = {http://dx.doi.org/10.1186/s12918-018-0556-z},
volume = {12},
year = {2018}
}
RIS format (EndNote, RefMan)
TY - JOUR
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.
AU - De,Meulder B
AU - Lefaudeux,D
AU - Bansal,AT
AU - Mazein,A
AU - Chaiboonchoe,A
AU - Ahmed,H
AU - Balaur,I
AU - Saqi,M
AU - Pellet,J
AU - Ballereau,S
AU - Lemonnier,N
AU - Sun,K
AU - Pandis,I
AU - Yang,X
AU - Batuwitage,M
AU - Kretsos,K
AU - van,Eyll J
AU - Bedding,A
AU - Davison,T
AU - Dodson,P
AU - Larminie,C
AU - Postle,A
AU - Corfield,J
AU - Djukanovic,R
AU - Chung,KF
AU - Adcock,IM
AU - Guo,Y-K
AU - Sterk,PJ
AU - Manta,A
AU - Rowe,A
AU - Baribaud,F
AU - Auffray,C
AU - U-BIOPRED,Study Group and the eTRIKS Consortium
DO - 10.1186/s12918-018-0556-z
PY - 2018///
SN - 1752-0509
TI - A computational framework for complex disease stratification from multiple large-scale datasets
T2 - BMC Systems Biology
UR - http://dx.doi.org/10.1186/s12918-018-0556-z
UR - https://www.ncbi.nlm.nih.gov/pubmed/29843806
UR - http://hdl.handle.net/10044/1/60119
VL - 12
ER -