The human epigenome has been experimentally characterized by thousands of measurements for every basepair in the human genome. We propose a deep neural network tensor factorization method, Avocado, that compresses this epigenomic data into a dense, information-rich representation. We use this learned representation to impute epigenomic data more accurately than previous methods, and we show that machine learning models that exploit this representation outperform those trained directly on epigenomic data on a variety of genomics tasks. These tasks include predicting gene expression, promoter-enhancer interactions, replication timing, and an element of 3D chromatin architecture.

中文翻译：

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鳄梨：一种多尺度深度张量分解方法学习人类表观基因组的潜在表示

人类表观基因组已通过对人类基因组中每个碱基对的数千次测量进行了实验表征。我们提出了一种深度神经网络张量分解方法 Avocado，它将表观基因组数据压缩成一个密集的、信息丰富的表示。我们使用这种学习表示来比以前的方法更准确地估算表观基因组数据，并且我们表明，利用这种表示的机器学习模型在各种基因组学任务中优于直接在表观基因组数据上训练的模型。这些任务包括预测基因表达、启动子-增强子相互作用、复制时间和 3D 染色质结构的一个元素。