DeepChrome: deep-learning for predicting gene expression from histone modifications
DeepChrome: deep-learning for predicting gene expression from histone modifications
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DOI：
10.1093/bioinformatics/btw427
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发表时间：
2016-09-01
期刊：
影响因子：
5.8
通讯作者：
Qi, Yanjun
中科院分区：
文献类型：
--
作者：
Singh, Ritambhara;Lanchantin, Jack;Qi, Yanjun
动机：组蛋白修饰是控制基因调节的最重要因素之一。从组蛋白修饰信号预测基因表达的计算方法对于理解它们在基因调控中的组合效应是非常期望的。这些知识可以帮助开发针对癌症等疾病的“表观遗传药物”。以前的研究用于量化组蛋白修饰和基因表达水平之间的关系，或者未能捕获组合效应，或者依赖于分离预测和组合分析的多种方法。本文使用深度卷积神经网络开发了一个统一的判别框架，以组蛋白修饰数据作为输入对基因表达进行分类。我们的系统名为DeepChrome，允许自动提取重要特征之间的复杂交互。为了同时可视化组蛋白修饰之间的组合相互作用，我们提出了一种新的基于优化的技术，该技术从学习的深度模型生成特征模式图。这提供了对调控基因的潜在表观遗传机制的直观描述。结果：我们表明，DeepChrome在REMC数据库中的56种不同细胞类型的基因表达分类任务上优于支持向量机和随机森林等最先进的模型。我们的可视化技术的输出不仅验证了以前的观察，但也允许新的见解组蛋白修饰标记之间的组合相互作用，其中一些最近已被观察到的实验研究。
Motivation: Histone modifications are among the most important factors that control gene regulation. Computational methods that predict gene expression from histone modification signals are highly desirable for understanding their combinatorial effects in gene regulation. This knowledge can help in developing 'epigenetic drugs' for diseases like cancer. Previous studies for quantifying the relationship between histone modifications and gene expression levels either failed to capture combinatorial effects or relied on multiple methods that separate predictions and combinatorial analysis. This paper develops a unified discriminative framework using a deep convolutional neural network to classify gene expression using histone modification data as input. Our system, called DeepChrome, allows automatic extraction of complex interactions among important features. To simultaneously visualize the combinatorial interactions among histone modifications, we propose a novel optimization-based technique that generates feature pattern maps from the learnt deep model. This provides an intuitive description of underlying epigenetic mechanisms that regulate genes.Results: We show that DeepChrome outperforms state-of-the-art models like Support Vector Machines and Random Forests for gene expression classification task on 56 different cell-types from REMC database. The output of our visualization technique not only validates the previous observations but also allows novel insights about combinatorial interactions among histone modification marks, some of which have recently been observed by experimental studies.