iPro-CSAF:基于卷积尖端神经网络和尖端注意力机制的推动者的识别
Qian Zhou1,2,3, Jie Meng1,2,3, Hao Luo4
1Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, China.
PeerJ. Computer science
|June 26, 2025
概括
iPro-CSAF是一种新的卷积尖端神经网络,可以准确地识别跨物种的DNA促进者. 这种计算方法在较少的参数下提供了高的识别精度,推动了基因表达研究.
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:
- 促进体是调节基因表达的关键DNA段.
- 准确的促进体鉴定对于理解基因调节和治疗遗传疾病至关重要.
- 现有的计算方法往往缺乏广泛的物种适用性,需要复杂的特征提取.
研究的目的:
- 开发一种有效的计算方法,用于促进者识别.
- 设计一种新的深度学习模型,用于识别DNA促进体.
- 为了提高各种物种中促进体识别的准确性和效率.
主要方法:
- 开发了iPro-CSAF,一个卷积尖端神经网络,具有尖端的注意力机制.
- 采用两个平行分支用于特征提取:尖端注意力和卷积尖端层.
- 通过对 prokaryotic 和 eukaryotic 物种进行广泛的促进者识别实验来评估性能.
主要成果:
- 与现有方法相比,iPro-CSAF表现出更高的性能,包括CNN,囊网络,注意力机制和LSTM/BiLSTM.
- 该模型在7个物种的原生生物和真核生物促进体识别中实现了高精度.
- iPro-CSAF显著优于需要先前生物或文本特征提取的方法.
结论:
- iPro-CSAF是一种有效的计算工具,用于促进者识别.
- 拟议的方法表现出低复杂度和优秀的概括能力.
- 这种方法推进了基因表达研究的计算基因组学领域.
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