表观基因组
代谢组
表观遗传学
生物
计算生物学
代谢组学
生物网络
表观遗传学
系统生物学
代谢网络
基因调控网络
信息基础设施
生物信息学
DNA甲基化
计算机科学
遗传学
数据科学
基因
基因表达
作者
Xuezhu Wang,Yucheng Dong,Yongchang Zheng,Yang Chen
标识
DOI:10.1016/j.jgg.2021.05.008
摘要
Genetic, epigenetic, and metabolic alterations are all hallmarks of cancer. However, the epigenome and metabolome are both highly complex and dynamic biological networks in vivo. The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy. From this perspective, we first review the state of high-throughput biological data acquisition (i.e. multiomics data) and analysis (i.e. computational tools) and then propose a conceptual in silico metabolic and epigenetic regulatory network (MER-Net) that is based on these current high-throughput methods. The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes, omics data acquisition, analysis of network information, and integration with validated database knowledge. Thus, MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks. We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.
科研通智能强力驱动
Strongly Powered by AbleSci AI