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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.

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