GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks
GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks
复制标题
GRNUlar：用于恢复单细胞基因调控网络的深度学习框架
DOI：
10.1089/cmb.2021.0437
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发表时间：
2022
影响因子：
1.7
通讯作者：
Aluru, Srinivas
中科院分区：
文献类型：
--
作者：
Shrivastava, Harsh;Zhang, Xiuwei;Song, Le;Aluru, Srinivas
我们提出了GRNUlar，一个新的深度学习框架，用于从单细胞rna测序（scRNA-Seq）数据中监督学习基因调控网络（grn）。我们的框架包含两个相互交织的模型。首先，我们通过开发一个多任务学习框架，利用神经网络的表达能力来捕获转录因子及其调节的相应基因之间的复杂依赖关系。其次，为了捕获在现实世界中观察到的grn的稀疏性，我们为我们的框架设计了一种展开算法技术。我们的深层架构需要对训练进行监督，为此，我们重新利用现有的合成数据模拟器，在底层GRN的指导下生成scRNA-Seq数据。实验结果表明，GRNUlar在合成数据集和真实数据集上都优于最先进的方法。我们的研究还证明了表达式数据模拟器在GRN推理的监督学习中的新颖和成功的应用。
We propose GRNUlar, a novel deep learning framework for supervised learning of gene regulatory networks (GRNs) from single-cell RNA-Sequencing (scRNA-Seq) data. Our framework incorporates two intertwined models. First, we leverage the expressive ability of neural networks to capture complex dependencies between transcription factors and the corresponding genes they regulate, by developing a multitask learning framework. Second, to capture sparsity of GRNs observed in the real world, we design an unrolled algorithm technique for our framework. Our deep architecture requires supervision for training, for which we repurpose existing synthetic data simulators that generate scRNA-Seq data guided by an underlying GRN. Experimental results demonstrate that GRNUlar outperforms state-of-the-art methods on both synthetic and real data sets. Our study also demonstrates the novel and successful use of expression data simulators for supervised learning of GRN inference.
影响因子：
23.9
作者：
Sarkar, Abby;Hochedlinger, Konrad
通讯作者：
Hochedlinger, Konrad
影响因子：
48
作者：
Pratapa, Aditya;Jalihal, Amogh P.;Murali, T. M.
通讯作者：
Murali, T. M.
影响因子：
5.8
作者：
Sanchez-Castillo, M.;Blanco, D.;Huang, Yufei
通讯作者：
Huang, Yufei