通过逆向调节的定向图形自编码器推断基因调节网络
Kaifu Long1, Junchang Xin2, Luxuan Qu3
1School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, 110819, China.
概括
我们开发了ARDGA,一种用于推断基因调节网络 (GRNs) 的新方法. ARDGA有效地捕获复杂的网络拓和基因表达数据属性,优于现有的方法.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 了解基因调节网络 (GRNs) 对于破译生物过程至关重要.
- 现有的GRN推断方法往往无法捕捉复杂的网络拓和基因表达数据的非相同分布.
研究的目的:
- 提出一种新的方法,即对抗性规则化定向图形自编码器 (ARDGA),用于准确的GRN推理.
- 解决当前处理复杂GRN结构和数据属性的方法的局限性.
主要方法:
- 计算了两个结构矩阵,以捕捉复杂拓的第一顺序和第二顺序近距离.
- 开发了一个消息传递模块与源和目标编码器来学习节点表示.
- 采用对抗性培训来规范目标载体到原始基因表达数据的先前分发.
主要成果:
- 与DREAM5数据集的强大基线相比,ARDGA表现优越.
- 在七个单细胞RNA测序 (scRNA-seq) 数据集上进行评估,ARDGA在不同基础真相网络中取得了具有竞争力的结果.
- 该方法有效地整合了基因表达数据的拓和统计特性.
结论:
- ARDGA为推断基因调节网络提供了一种强大而有效的方法.
- 该方法通过解决网络复杂性和数据分布的关键挑战,推动了GRN推断领域的发展.
- 公开可用的代码有助于进一步的研究和应用.
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