用于识别增强剂-促进剂相互作用的计算方法
Haiyan Gong1, Zhengyuan Chen1, Yuxin Tang1
1School of Computer and Communication Engineering Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing 100083 China.
Quantitative biology (Beijing, China)
|February 12, 2026
概括
本综述总结了识别增强剂-促进剂相互作用 (EPI) 的方法,这些相互作用对基因调节至关重要. 它强调了深度学习的进步,并为研究人员研究这些基因组元素及其在癌症等疾病中的作用提供了框架.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 增强剂-促进剂相互作用 (EPI) 是人类基因组的 cis 调节机制的关键组成部分.
- 识别EPI对于理解基因表达调节至关重要.
- 目前用于检测EPI的方法需要进行系统审查,以帮助研究人员进行应用和优化.
研究的目的:
- 为识别增强剂-促进剂相互作用 (EPI) 的方法提供全面的审查.
- 描述预测EPI的框架,并总结现有的数据集和预测工具.
- 审查EPI识别方法在疾病环境中的应用,特别是癌症.
主要方法:
- 自2010年以来,对EPI识别的测序技术和计算模型进行系统审查.
- 基于数据特征 (遗传,基因组,表观基因组) 的预测方法的分类.
- 机器学习和深度学习方法的评估,包括转移学习,用于EPI预测.
主要成果:
- EPI在调节基因表达方面发挥着至关重要的作用.
- 许多计算方法,包括深度学习模型,已被开发用于预测增强剂,促进剂及其相互作用.
- 总结了访问相关数据集和工具的网站.
- EPI识别方法越来越多地用于研究癌症等疾病.
结论:
- 计算机技术的进步,特别是深度学习和转移学习,使得从各种基因组特征来准确预测EPI.
- 深度学习模型可以从DNA序列直接预测EPI,减少研究人员的计算时间.
- 本综述为进入增强剂-促进剂相互作用研究领域的科学家提供了详细的研究框架.
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