复杂疾病的组学数据挖掘方法研究

摘要

目前针对单独某一类型的组学数据,已挖掘出部分与肿瘤真实相关的遗传因素及环境因素等信息,但仍然可能仅是隐藏于复杂遗传机制背后的冰山一角,导致这种局限性的关键原因可能是疾病模型过于简化即忽略多层次组学数据之间的相互关系.研究认为在加深理解全基因组SNP数据的基础上,进一步融合多源组学数据,加深理解上位性、异质性等现象,从而提高肿瘤风险评估能力,有利于实现个体化医疗目标.本文从SNP数据和多源组学数据分析的角度比较分析现有复杂疾病的组学数据挖掘方法.%At present, for a single type of omics data, part of the real genetic and environmental factors associated with the tumor has been excavated, but some still may only be hidden in the complex genetic mechanism behind the tip of the iceberg, The key reason to lead to the limitations may be that the disease model is too simplistic, namely, to ignore the interrelationships between multi-level histological data.Studies thank that deepening the understanding of genome SNP data, further integrating omulti-source histological data, deeply understanding epistasis, heterogeneity and other phenomena, and thereby enhancing the ability of cancer risk assessment, is conducive to the realization of personalized medical goals.This paper analyzes the present data mining methods of complex diseases from the perspective of SNP data and multi-source data analysis.