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使用预训练的Resnet-50和GoogleNet模型和13层CNN模型进行人类外子和内子的分类

Feriel Ben Nasr Barber¹, Afef Elloumi Oueslati¹

¹Electrical Engineering Department, SITI Laboratory, National School of Engineers of Tunis (ENIT), BP37, Le Belvedere, 1002 Tunis, Tunisia; Electrical Engineering Department, National School of Engineers of Carthage (ENICarthage), Tunis, Tunisia.

Journal, genetic engineering & biotechnology

|March 17, 2024

概括

深度学习模型,包括新的CNN,分析以图像形式表示的人类基因组序列. 拟议的CNN实现了高精度 (91.6%) 和高效的执行时间,超过了GoogleNet.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

分析人类基因组序列,包括数百万核酸的外子和内子,具有显著的复杂性.
基因组学研究受益于先进的计算工具,如信号处理和深度学习,用于预测建模.

研究的目的:

开发和评估深度学习模型来分类人类外子和内子序列.
在基因组序列分析中评估预训练和新型卷积神经网络 (CNN) 模型的效率和准确性.

主要方法:

人类的外子和内子被转换成彩色图像使用频率混沌游戏表示.
使用了三个卷积神经网络 (CNN) 模型:Resnet-50,GoogleNet和一个定制的13个隐藏层CNN.
这些模型经过训练,并对它们在分类基因组序列图像的准确性和执行时间进行了评估.

主要成果:

在7小时的执行时间内,Resnet-50实现了92%的准确性.
谷歌网在2.5小时内实现了91.5%的准确性.
拟议的CNN模型显示了91.6%的准确性,执行时间为2小时37分钟.

结论:

拟议的CNN模型提供了具有竞争力的准确率,略高于GoogleNet.
在执行速度方面,拟议的CNN模型比Resnet-50快得多.

关键词:

在美国,CNN是CNN.分类分类分类分类.前子是指前子的前子.这是谷歌的GoogleNet.这里面是Introns.复网-50-50 复网-50 复网-50 复网-50 复网

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