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iPro2L-DG:基于改进的密度网络和全球关注机制的混合网络,用于识别促进器序列

Rufeng Lei¹, Jianhua Jia¹, Lulu Qin¹

¹School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China.

|March 21, 2024

概括

一个新的计算模型,iPro2L-DG,准确地预测了基因促进者及其表达强度. 这种深度学习工具为基因表达研究提供了比传统的测序方法更快,更有效的替代方案.

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:

促使者是调节基因转录启动和表达水平的关键DNA序列.
准确的促进体鉴定对于基因表达研究至关重要.
促进者识别的传统测序方法昂贵且耗时,需要计算方法.

研究的目的:

开发一种新的计算模型,iPro2L-DG,用于准确的促进体识别和促进体强度的预测.
利用深度学习技术来提高推动者预测的效率和准确性.

主要方法:

该iPro2L-DG模型使用了改进的密集连接卷积网络 (DenseNet) 和全球注意力机制 (GAM).
促进子序列使用C2编码和核酸化学性质 (NCP) 编码的组合进行编码.
改进的DenseNet提取了高级功能,而GAM评估了功能的重要性,随后是用于预测的全连接神经网络 (FNN).

主要成果:

该iPro2L-DG模型实现了94.10%的准确性和0.8833的马修斯相关系数促进者识别.
对于促进器强度预测,该模型显示了89.42%的准确性,马修斯相关系数为0.7915.
在促进者识别和强度预测任务中,iPro2L-DG显著超过了现有的预测器.

结论:

iPro2L-DG预测器是一个先进的计算工具,用于促进者识别和强度预测.
与传统方法相比,该模型提供了更有效和更准确的解决方案,推动了基因表达研究.

关键词:

在DenseNet中,使用的是DenseNet.编码编码编码全球关注机制全球关注机制活动主办人活动主办人发起者的强度是强大的.

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