转基因选择器 (TransGeneSelector):使用变压器方法,从植物对各种环境的反应中从小型转录基因数据集中挖掘关键基因
Kerui Huang1, Jianhong Tian2, Lei Sun3
1Key Laboratory of Agricultural Products Processing and Food Safety in Hunan Higher Education, Hunan University of Arts and Science, Changde, 415000, China.
BMC genomics
|March 18, 2025
概括
一个新的深度学习框架TransGeneSelector有效地从小型植物转录基因数据集中识别关键基因. 这种方法有助于理解环境适应的基因调节,优于传统方法.
科学领域:
- 植物分子生物学 植物分子生物学
- 生物信息学是一种生物信息学.
- 计算生物学是一种计算生物学.
背景情况:
- 基因挖掘对于了解植物对环境线索的反应至关重要.
- 传统的机器学习在转录学中与小样本大小和复杂的基因相互作用作斗争.
- 深度学习模型通常不适合有限的转录基因数据.
研究的目的:
- 介绍TransGeneSelector,这是一个深度学习框架,用于在小型转录基因数据集中识别关键基因.
- 从有限的数据中捕捉基因调节动态的现有方法的局限性.
- 提高对环境压力下的植物适应机制的理解.
主要方法:
- 开发了TransGeneSelector,将Wasserstein生成对抗网络与梯度惩罚 (WGAN-GP) 集成到数据增强中.
- 采用基于变压器的网络进行基因分类和特征提取.
- 利用Shapley添加式解释 (SHAP) 来识别上游调节基因.
- 构建基因调节网络以分析已识别的基因功能.
主要成果:
- TransGeneSelector在预测Arabidopsis thaliana的种子发芽和热应激条件方面取得了很高的准确性.
- 该模型在分类任务中表现优于随机森林和支持矢量机 (SVM).
- 鉴定到的基因被丰富了种子发芽和热应激反应的关键途径.
- RT-qPCR验证证实了基因选择和表达模式的准确性.
结论:
- TransGeneSelector提供了一个强大的深度学习解决方案,用于在小型转录数据集中进行基因挖掘.
- 该框架为基因调节和植物适应提供了更深入的见解.
- 这种方法有助于识别参与环境反应的关键基因.
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