Predicting gene regulatory interactions based on spatial gene expression data and deep learning

Yang Yang; Qingwei Fang; Hong-Bin Shen

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Predicting gene regulatory interactions based on spatial gene expression data and deep learning

机译：基于空间基因表达数据和深度学习预测基因调控相互作用

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Gene expression images, with abundant spatial expression patterns, have become an important resource for identifying gene regulatory networks (GRNs), while the computational methods for image-based GRN reconstruction have been very few. In spite of the difference in experimental types and conditions, we utilize previously verified TF-gene interactions by RNA-Seq data and motif analysis as training labels, and design a supervised deep learning method, GripDL, for the prediction of GRNs using gene expression images. We demonstrate its performance by inferring large-scale GRNs for Drosophila eye development based on spatial expression patterns of Drosophila embryos. The GRNs constructed by GripDL not only show high consistency with previous work, but also reveal important regulators in the early stage of Drosophila eye formation.

机译：具有丰富的空间表达模式的基因表达图像已成为鉴定基因调控网络（GRN）的重要资源，而基于图像的GRN重建的计算方法却很少。尽管实验类型和条件存在差异，我们还是利用先前通过RNA-Seq数据和模体分析验证的TF基因相互作用作为训练标签，并设计了一种有监督的深度学习方法GripDL，用于使用基因表达图像预测GRN 。我们通过基于果蝇胚胎的空间表达模式推断果蝇眼发育的大规模GRNs来证明其性能。由GripDL构建的GRN不仅显示出与先前工作的高度一致性，而且还揭示了果蝇眼形成早期的重要调控因子。

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《PLoS Computational Biology》 |2019年第9期|共19页
作者
Yang Yang; Qingwei Fang; Hong-Bin Shen;
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中图分类细胞生物学;
关键词
Gene regulationGene expressionDrosophila melanogasterEye developmentEmbryosImaging techniquesTranscriptional controlGene regulatory networks;

机译：基因调控基因表达黑腹果蝇眼发育胚胎成像技术转录控制基因调控网络;

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专利

1. A computational method of predicting regulatory interactions in Arabidopsis based on gene expression data and sequence information [J] . Xiaoqing Yu, Hongyun Gao, Xiaoqi Zheng, Computational biology and chemistry . 2014,第auga期

机译：基于基因表达数据和序列信息的拟南芥中调控相互作用的计算方法
2. LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data [J] . Geeven Geert, MacGillavry Harold D., Eggers Ruben, Nucleic Acids Research . 2011,第13期

机译：LLM3D：一种基于对数线性建模的方法，可从全基因组表达数据预测功能基因调节相互作用
3. LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data [J] . August B. Smit, Geert Geeven, Harold D. MacGillavry, Nucleic acids research . 2011,第13期

机译：LLM3D：一种基于对数线性建模的方法，可从全基因组表达数据预测功能基因调节相互作用
4. A Deep Learning Framework for Identifying Essential Proteins Based on Protein-Protein Interaction Network and Gene Expression Data [C] . Min Zeng, Min Li, Zhihui Fei, IEEE International Conference on Bioinformatics and Biomedicine . 2018

机译：基于蛋白质-蛋白质相互作用网络和基因表达数据识别必需蛋白质的深度学习框架
5. Reverse engineering of gene regulatory networks for discovery of novel interactions in pathways using gene expression data. [D] . Habib, Tanwir. 2009

机译：基因调控网络的逆向工程，可利用基因表达数据发现途径中的新型相互作用。
6. Predicting gene regulatory interactions based on spatial gene expression data and deep learning [O] . Yang Yang, Qingwei Fang, Hong-Bin Shen 2019

机译：基于空间基因表达数据和深度学习预测基因调控相互作用
7. Predicting gene regulatory interactions based on spatial gene expression data and deep learning [O] . Yang Yang, Qingwei Fang, Hong-Bin Shen 2019

机译：基于空间基因表达数据和深度学习预测基因调节相互作用

Predicting gene regulatory interactions based on spatial gene expression data and deep learning

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