Leverage Large-Scale Biological Networks to Decipher the Genetic Basis of Human Diseases Using Machine Learning.
Leverage Large-Scale Biological Networks to Decipher the Genetic Basis of Human Diseases Using Machine Learning.
复制标题
利用大规模生物网络，利用机器学习破译人类疾病的遗传基础。
DOI：
10.1007/978-1-0716-0826-5_11
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Wang,Jianrong
中科院分区：
文献类型：
--
作者：
Wang,Hao;Yang,Jiaxin;Wang,Jianrong
精准医学的一个基本问题是定量解码复杂人类疾病的遗传基础，这将使基于个人基因组序列的疾病风险预测模型的开发成为可能。为了解释不同细胞环境中的复杂系统，大规模调控网络是整合到分析中的关键组成部分。基于多组学和疾病遗传学数据的快速积累，先进的机器学习算法和高效的计算工具正在成为从基因型预测表型、识别潜在致病遗传变异和揭示疾病机制的驱动力。在这里，我们回顾了这一主题的最先进的方法，并描述了一个计算管道，该管道将一系列算法组装在一起，通过逐步描绘调控电路来实现改进的疾病遗传学预测。
A fundamental question in precision medicine is to quantitatively decode the genetic basis of complex human diseases, which will enable the development of predictive models of disease risks based on personal genome sequences. To account for the complex systems within different cellular contexts, large-scale regulatory networks are critical components to be integrated into the analysis. Based on the fast accumulation of multiomics and disease genetics data, advanced machine learning algorithms and efficient computational tools are becoming the driving force in predicting phenotypes from genotypes, identifying potential causal genetic variants, and revealing disease mechanisms. Here, we review the state-of-the-art methods for this topic and describe a computational pipeline that assembles a series of algorithms together to achieve improved disease genetics prediction through the delineation of regulatory circuitry step by step.