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一个混合的1DCNN-GRU深度学习框架,用于使用单细胞转录组学对山羊粒粉细胞生育潜力的分类

Thanida Sananmuang¹, Denis Puthier², Kaj Chokeshaiusaha¹

¹Department of Veterinary Science, Faculty of Veterinary Medicine, Rajamangala University of Technology Tawan-OK, Chonburi, Thailand.

Veterinary world

|September 10, 2025

概括

这项研究开发了一种深度学习模型,使用单细胞RNA测序对山羊颗粒细胞 (GCs) 进行生育分类. 该模型准确地区分了支持生育的GC,为畜牧养殖提供了一个新的工具.

科学领域:

生殖生物学生殖生物学
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

粒状细胞 (GCs) 对于山羊的卵泡发育和卵细胞质量至关重要.
GC基因表达特征可以表明生育能力,但缺乏标准化的评估方法.
转录组数据为生育提供了潜在的生物标志物,但需要强大的分析工具.

研究的目的:

开发一种混合深度学习模型,根据生育潜力对山羊GC进行分类.
为了利用单细胞RNA测序 (scRNA-seq) 数据进行GC分类.
创建一种可量化的方法,用基因表达来评估GC质量.

主要方法:

对公开可用的山羊scRNA-seq数据集的分析.
鉴定44个差异表达基因 (DEGs),以区分支持生育 (FS) 和非支持生育 (NFS) 的GCs.
使用DEG表达式配置文件进行混合1DCNN-GRU深度学习模型的培训和评估.

主要成果:

混合型1DCNN-GRU模型实现了高分类性能 (精度98.89%,F1得分98.84%).
该模型在多菌性山羊 (87%) 与单菌性山羊 (10.17%) 相比,发现FS-GC的比例显著更高.
DEG分析证实了模型的生物一致性和跨数据集的概括性.

结论:

这项研究引入了第一个基于深度学习的山羊GCs的分类,使用scRNA-seq.
开发的1DCNN-GRU模型提供了一种可靠和可量化的方法来评估GC生育能力.
这种方法有望提高生殖选择和精确畜牧管理.

关键词:

一个DCNN-GRU模型.深度学习是一种深度学习.不同的基因表达.羊的生育能力羊的生育能力颗粒状细胞是颗粒状细胞.一个单细胞RNA测序.

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