深度FGRN:基于定向图嵌入的调节类型的基因调节网络推断
Zhen Gao1, Yansen Su2, Junfeng Xia3
1The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China.
Briefings in bioinformatics
|April 6, 2024
概括
研究人员开发了DeepFGRN,这是一种深度学习模型,可以通过方向和类型重建详细的基因调节网络 (GRNs). 这促进了系统生物学的进步,使疾病生物标志物和潜在的治疗点能够更好地识别.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 从基因表达数据推断基因调节网络 (GRN) 在系统生物学中至关重要.
- 由于数据限制和方法缺陷,现有的计算方法往往无法重建具有监管类型的定向GRN.
研究的目的:
- 提出DeepFGRN,这是一个新的深度学习模型,用于重建细粒度基因调节网络 (FGRNs),包括调节类型和方向.
- 解决大型,稀疏和定向的GRNs在真实生物系统中所带来的挑战.
主要方法:
- DeepFGRN使用一个节点双向表示模块用于定向图嵌入.
- 它包含源和目标生成器,用于学习基因邻居嵌入和对抗性学习策略.
- 一个相关性分析模块被用来提取基因表达特征,并捕获调节器和基因之间的相关性.
主要成果:
- 在GRN和FGRN推断中,DeepFGRN表现出了竞争力的表现.
- 该模型成功地将指导GRNs与监管类型重建.
- 确定了潜在的生物标志物和治疗乳腺癌,肝癌,肺癌和COVID-19的治疗药物.
结论:
- DeepFGRN提供了一个强大的方法来推断详细的基因调节网络.
- 鉴定的生物标志物和治疗点为疾病治疗研究提供了新的途径.
- 这项工作推进了GRN推断领域及其在精密医学中的应用.
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