DEEPSEN：一种基于卷积神经网络的超级增强子预测方法。
DEEPSEN: a convolutional neural network based method for super-enhancer prediction.
机构信息
Department of Computer Science and Technology, Tongji University, 4800 Cao'an Road, Shanghai, 201804, China.
School of Computer Science and Technology, Donghua University, 2999 North Renmin Road, Shanghai, 201620, China.
出版信息
BMC Bioinformatics. 2019 Dec 24;20(Suppl 15):598. doi: 10.1186/s12859-019-3180-z.
BACKGROUND
Super-enhancers (SEs) are clusters of transcriptional active enhancers, which dictate the expression of genes defining cell identity and play an important role in the development and progression of tumors and other diseases. Many key cancer oncogenes are driven by super-enhancers, and the mutations associated with common diseases such as Alzheimer's disease are significantly enriched with super-enhancers. Super-enhancers have shown great potential for the identification of key oncogenes and the discovery of disease-associated mutational sites.
RESULTS
In this paper, we propose a new computational method called DEEPSEN for predicting super-enhancers based on convolutional neural network. The proposed method integrates 36 kinds of features. Compared with existing approaches, our method performs better and can be used for genome-wide prediction of super-enhancers. Besides, we screen important features for predicting super-enhancers.
CONCLUSION
Convolutional neural network is effective in boosting the performance of super-enhancer prediction.
背景
超级增强子（SEs）是转录活跃增强子的簇，决定着定义细胞身份的基因的表达，并在肿瘤和其他疾病的发生和发展中起着重要作用。许多关键的癌症致癌基因受超级增强子驱动，与阿尔茨海默病等常见疾病相关的突变明显富含超级增强子。超级增强子在鉴定关键致癌基因和发现与疾病相关的突变位点方面显示出巨大的潜力。
结果
在本文中，我们提出了一种新的基于卷积神经网络的名为 DEEPSEN 的预测超级增强子的计算方法。该方法集成了 36 种特征。与现有方法相比，我们的方法性能更好，可用于超级增强子的全基因组预测。此外，我们筛选出用于预测超级增强子的重要特征。
结论
卷积神经网络在提高超级增强子预测的性能方面是有效的。
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