一种新的可解释的深度转移学习,结合了各种可学习的参数,以基于单细胞基因调节网络的改进T2D预测
Sumaya Alghamdi1,2, Turki Turki3
1Department of Computer Science, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
Scientific reports
|February 23, 2024
概括
本研究引入了一种可解释的深度转移学习方法,用于使用单细胞基因调节网络图像进行2型糖尿病 (T2D) 预测. 新的TFeSEResNeXT101模型实现了0.97平衡精度,显著改善了基线模型.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 机器学习 机器学习
背景情况:
- 准确的深度学习 (DL) 模型用于预测2型糖尿病 (T2D) 需要有效的特征表示和可解释性.
- 现有的DL工具往往缺乏可解释性,阻碍了性能解释和改进.
研究的目的:
- 开发一种可解释的深度转移学习 (DTL) 方法,使用单细胞基因调节网络 (SCGRN) 图像来预测T2D.
- 为了提高性能,并为DL模型在T2D检测中提供解释性.
主要方法:
- 在DTL中使用预训练模型 (SEResNet152,SEResNeXT101) 进行知识传输.
- 根据SCGRN图像进行T2D分类的Adam优化器进行了调整分类层.
- 实施了两个DTL策略:结卷积基重和微调连续层.
主要成果:
- TFeSEResNeXT101模型实现了最高的平均平衡精度 (BAC) 0.97.
- 超过基线模型的表现,这些模型的平均BAC为0.86.
- 模拟研究证实了亚当优化器与预训练重量分配一致性所产生的优势.
结论:
- 拟议的可解释的DTL方法显著提高了T2D预测的准确性.
- 该模型的可解释性有助于理解和改进其预测性能.
- 这种方法为开发可靠的T2D诊断工具提供了有希望的方向.
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