卷积神经网络
残差神经网络
残余物
深度学习
人工神经网络
增强子
计算机科学
人工智能
比例(比率)
计算生物学
基因组
机器学习
模式识别(心理学)
生物
算法
基因
遗传学
地理
地图学
基因表达
作者
Sara Sabba,Meroua Smara,Mehdi Benhacine,Amina Hameurlaine
出处
期刊:Springer eBooks
[Springer Nature]
日期:2022-01-01
卷期号:: 32-42
被引量:1
标识
DOI:10.1007/978-3-030-96311-8_4
摘要
Residual neural network (ResNet) is a Deep Learning model introduced by He et al. [13] in 2015 to enhance traditional Convolutional neural networks for computer vision problems. It uses skip connections over some layer blocks to avoid vanishing gradient problem. Currently, many researches are focused to test and prove the efficiency of the ResNet on different domains such as genomics. In this paper, we propose a new ResNet model for predicting super-enhancers on genome scale. In fact, the prediction of super-enhancers (SEs) has prominent roles in biological and pathological processes; especially that related to the detection and progression of tumors. The obtained results are very promising and they proved the performance of our proposal compared to the CNN results.
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