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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Using Convolutional Neural Network to Study the Regulatory Relationship Between DNA Methylation and Gene Expression
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Using Convolutional Neural Network to Study the Regulatory Relationship Between DNA Methylation and Gene Expression

机译：使用卷积神经网络研究DNA甲基化与基因表达之间的调控关系

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摘要

In this work, we propose a novel framework based on convolutional neural networks to integrate information of DNA methylation and DNA sequences for studying their relationship with gene expression. Using the data from 8 cell types, we show that the trained models provide higher explained variation of gene expression from DNA methylation (up to 30%) compared to methods that only consider one genomic data type or from one region (up to 12%). In addition, the extracted sequence motifs from the filters of the first convolutional layers reveal the involvement of regulatory modulators including DNA Methyltransferase 1 (DNMT1) and other transcription factors, shedding light on the complex regulatory mechanisms of DNA methylation. We found that more than half of the 20 most enriched motifs correspond to regulators that are well supported by previous publications and ChIP-Seq transcription factor binding datasets. Our work has demonstrated the usefulness of convolutional neural networks in revealing complex relationships from omics data.
机译：在这项工作中，我们提出了一个基于卷积神经网络的新颖框架，以整合DNA甲基化和DNA序列的信息，以研究它们与基因表达的关系。与仅考虑一种基因组数据类型或一种地区（高达12％）的方法相比，使用来自8种细胞类型的数据，我们显示，经过训练的模型提供了从DNA甲基化（高达30％）到更高的基因表达解释变异。 。另外，从第一卷积层的滤膜中提取的序列基序揭示了包括DNA甲基转移酶1（DNMT1）和其他转录因子在内的调控调节剂的参与，从而揭示了DNA甲基化的复杂调控机制。我们发现20个最丰富的基序中有超过一半对应于以前的出版物和ChIP-Seq转录因子结合数据集很好地支持的调节子。我们的工作证明了卷积神经网络在揭示组学数据中复杂关系方面的有用性。

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