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Integrative analysis of methylation and transcriptional profiles to predict aging and construct aging specific cross-tissue networks

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...

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Detalles Bibliográficos
Autores principales: Wang, Yin, Huang, Tao, Xie, Lu, Liu, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5260078/
https://www.ncbi.nlm.nih.gov/pubmed/28155676
http://dx.doi.org/10.1186/s12918-016-0354-4
Descripción
Sumario: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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0354-4) contains supplementary material, which is available to authorized users.