Dataset Information
Integrative analysis of methylation and transcriptional profiles to predict aging and construct aging specific cross-tissue networks.
Ontology highlight
ABSTRACT: Background
Aging is a complex process relating multi-scale omics data. Finding key age markers in normal tissues could help to provide reliable aging predictions in human. However, predicting age based on multi-omics data with both accuracy and informative biological function has not been performed systematically, thus relative cross-tissue analysis has not been investigated entirely, either.Results
Here we have developed an improved prediction pipeline, the Integrating and Stepwise Age-Prediction (ISAP) method, to regress age and find key aging markers effectively. Furthermore, we have performed a serious of network analyses, such as the PPI network, cross-tissue networks and pathway interaction networks.Conclusion
Our results find important coordinated aging patterns between different tissues. Both co-profiling and cross-pathway analyses identify more thorough functions of aging, and could help to find aging markers, pathways and relative aging disease researches.
SUBMITTER: Wang Y
PROVIDER: S-EPMC5260078 | biostudies-literature | 2016 Dec
REPOSITORIES: biostudies-literature
Publications
Integrative analysis of methylation and transcriptional profiles to predict aging and construct aging specific cross-tissue networks.
BMC systems biology 20161223 Suppl 4
<h4>Background</h4>Aging is a complex process relating multi-scale omics data. Finding key age markers in normal tissues could help to provide reliable aging predictions in human. However, predicting age based on multi-omics data with both accuracy and informative biological function has not been performed systematically, thus relative cross-tissue analysis has not been investigated entirely, either.<h4>Results</h4>Here we have developed an improved prediction pipeline, the Integrating and Stepw ...[more]
