论文标题
DeepChrome 2.0：研究和改进体系结构，可视化和实验
DeepChrome 2.0: Investigating and Improving Architectures, Visualizations, & Experiments
论文作者
论文摘要
组蛋白修饰在基因调节中起关键作用。因此，从组蛋白修饰信号中预测基因表达是表观遗传学中的一个高度动机问题。我们基于Singh等人的Deepchrome作品。 （2016年），他训练了将组蛋白修饰信号映射到基因表达的分类器。我们提出了一种新颖的可视化技术，以提供有关基因调节组蛋白修饰之间组合关系的见解，该基因调节使用生成性对抗网络来生成组蛋白修饰信号。我们还探索和比较了各种架构变化，结果表明，来自DeepChrome的645K参数卷积神经网络具有与12参数线性网络相同的预测能力。跨细胞预测实验的结果，该模型在不同大小，细胞类型和相关性的数据集上进行了训练和测试，这表明组蛋白修饰信号与基因表达之间的关系与细胞类型无关。我们在github \ footNote上释放deepchrome的pytorch重新实现{\ url {github.com/sssssss1029/gene_expression_294}}。\ parfillskip = 0pt = 0pt
Histone modifications play a critical role in gene regulation. Consequently, predicting gene expression from histone modification signals is a highly motivated problem in epigenetics. We build upon the work of DeepChrome by Singh et al. (2016), who trained classifiers that map histone modification signals to gene expression. We present a novel visualization technique for providing insight into combinatorial relationships among histone modifications for gene regulation that uses a generative adversarial network to generate histone modification signals. We also explore and compare various architectural changes, with results suggesting that the 645k-parameter convolutional neural network from DeepChrome has the same predictive power as a 12-parameter linear network. Results from cross-cell prediction experiments, where the model is trained and tested on datasets of varying sizes, cell-types, and correlations, suggest the relationship between histone modification signals and gene expression is independent of cell type. We release our PyTorch re-implementation of DeepChrome on GitHub \footnote{\url{github.com/ssss1029/gene_expression_294}}.\parfillskip=0pt
