不确定性量化和温度缩放校准用于蛋白质-RNA结合位预测
Ximin Zeng1, Hongmei Wang1, Long Zhao1
1Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China.
Journal of chemical information and modeling
|June 2, 2025
概括
这项研究引入了一种新的方法,通过减少模型不确定性来改善蛋白质-RNA结合部位的预测. 温度缩放和分方法提高了这些关键预测的准确性和可靠性.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 深度学习应用程序深度学习应用程序
背景情况:
- 深度学习模型经常充当黑子,引发人们对预测可靠性和不确定性的担忧.
- 蛋白质-RNA相互作用至关重要,但预测结合位点的现有计算方法缺乏不确定性量化 (UQ).
研究的目的:
- 开发一种可靠的方法,以提高可靠性来预测蛋白质-RNA结合部位.
- 使用UQ技术量化和减少预测不确定性.
主要方法:
- 建议MGCA (多尺度图形卷积网络,卷积神经网络和注意力) 改进特征提取.
- 使用预期校准错误 (ECE) 和温度缩放 (TS) 实现了UQ.
- 引入了一种基于ECE的新型分隔选方法,以评估不确定性降低.
主要成果:
- 在蛋白质-RNA结合部位预测方面,MGCA取得了竞争力的结果.
- 分裂箱方法有效地减少了假阳性 (FP).
- 温度缩放显著降低了模型ECE,当与分割桶方法相结合时,提高了预测精度.
结论:
- 温度缩放有效降低蛋白质-RNA结合部位预测的不确定性.
- 尽量减少模型不确定性可以明显提高预测质量和可靠性.
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