The self-organization model reveals systematic characteristics of aging

Aging is a fundamental biological process, where key bio-markers interact with each other and synergistically regulate the aging process. Thus aging dysfunction will induce many disorders. Finding aging markers and re-constructing networks based on multi-omics data (i.e. methylation, transcriptional and so on) are informative to study the aging process. However, optimizing the model to predict aging have not been performed systemically, although it is critical to identify potential molecular mechanism of aging relative diseases. This paper aims to model the aging self-organization system using a serious of supervised learning methods, and study complex molecular mechanism of aging at system level: i.e. optimizing the aging network; summarizing interactions between aging markers; accumulating patterns of aging markers within module; finding order-parameters of the aging self-organization system. In this work, the normal aging process is modeled based on multi-omics profiles across tissues. In addition, the computational pipeline aims to model aging self-organizing systems and study the relationship between aging and related diseases (i.e. cancers), thus provide useful indexes of aging related diseases and improve diagnostic effects for both pre- and pro- gnosis.

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