A computational framework for complex disease stratification from multiple large-scale datasets.
<p><strong>Background</strong><br/> 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...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Drugi avtorji: | |
| Format: | Journal article |
| Jezik: | English |
| Izdano: |
BioMed Central
2018
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| Izvleček: | <p><strong>Background</strong><br/> 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.</p><br/> <p><strong>Methods</strong><br/> The framework is divided into four major steps: dataset subsetting, feature filtering, ‘omics-based clustering and biomarker identification.</p><br/> <p><strong>Results</strong><br/> 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.</p><br/> <p><strong>Conclusions</strong><br/> 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.</p><br/> |
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