Abstract：
Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.
摘要：
鉴于转录因子（TF）结合变化带来的复杂性，了解顺式调节元件对基因调节的影响提出了许多挑战。染色质可及性，结构限制，和细胞类型的差异。这篇综述讨论了基因调控网络在增强对转录调控的理解中的作用，并涵盖了从基于表达的方法到有监督的机器学习的构建方法。此外,关键的实验方法，包括MPRA和基于CRISPR-Cas9的筛查，这显著有助于理解TF结合偏好和顺式调节元件功能，正在探索。最后,分析了机器学习和人工智能解开顺式监管逻辑的潜力。这些计算上的进步对精准医学有着深远的影响，治疗靶点发现，以及健康和疾病遗传变异的研究。
