BACKGROUND 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 related diseases. METHODS This paper aims to model the aging self-organization system using a series of supervised learning methods, and study complex molecular mechanisms 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 in the aging self-organization system. RESULTS In this work, the normal aging process is modeled based on multi-omics profiles across different 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 indicators of aging related diseases and could help to improve prediction abilities of diagnostics. CONCLUSIONS The aging process could be studied thoroughly by modelling the self-organization system, where key functions and the crosstalk between aging and cancers were identified.

中文翻译：

---

自组织模型揭示了衰老的系统特征。

背景技术衰老是基本的生物学过程，其中关键的生物标志物彼此相互作用并协同调节衰老过程。因此，衰老的功能障碍会诱发许多疾病。根据多组学数据（即甲基化，转录等）找到衰老标记并重建网络对于研究衰老过程是有益的。然而，优化模型以预测衰老并没有系统地进行，尽管确定衰老相关疾病的潜在分子机制至关重要。方法本文旨在通过一系列有监督的学习方法对衰老自组织系统进行建模，并在系统水平上研究衰老的复杂分子机制：即优化衰老网络；优化衰老网络。总结老化标记之间的相互作用；在模块内累积老化标记的模式；在衰老的自组织系统中寻找顺序参数。结果在这项工作中，正常的衰老过程是基于跨不同组织的多组学概况建模的。此外，该计算管道旨在对衰老的自组织系统进行建模，并研究衰老与相关疾病（即癌症）之间的关系，从而为衰老相关疾病提供有用的指标，并有助于提高诊断的预测能力。结论可以通过对自组织系统建模来彻底研究衰老过程，该系统可以识别衰老与癌症之间的关键功能以及相互影响。正常的衰老过程是基于跨不同组织的多组学概况建模的。此外，该计算管道旨在对衰老的自组织系统进行建模，并研究衰老与相关疾病（即癌症）之间的关系，从而为衰老相关疾病提供有用的指标，并有助于提高诊断的预测能力。结论可以通过对自组织系统建模来彻底研究衰老过程，该系统可以识别衰老与癌症之间的关键功能以及相互影响。正常的衰老过程是基于跨不同组织的多组学概况建模的。此外，该计算管道旨在对衰老的自组织系统进行建模，并研究衰老与相关疾病（即癌症）之间的关系，从而为衰老相关疾病提供有用的指标，并有助于提高诊断的预测能力。结论可以通过对自组织系统建模来彻底研究衰老过程，该系统可以识别衰老与癌症之间的关键功能以及相互影响。