Imperial College London
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DrKaiSun

Faculty of Engineering, Department of Computing

DSI Institute Manager
 
 
 
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Contact

 

k.sun Website

 
 
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Location

 

William Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

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}
}
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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  -
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