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Updated: Jan 9, 2026

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Mechanical Control of Relaxation Using Intact Cardiac Trabeculae
Published on: February 17, 2023
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可解释的热力学基于分数的放松游览分类
Benjamin Kuznets-Speck1,2,3,4, Jaekwon Jung1,2,3,4,5, Pornchanan Pholraksa1,2,3,4
1Department of Cell & Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
bioRxiv : the preprint server for biology
|December 11, 2025
概括
我们介绍了Keeping SCORE,这是一种新的方法,可以将扩散模型转换为准确的分类和回归. 这种方法为计算生物学中的可解释AI提供不确定性估计和特征归属.
科学领域:
- 计算生物学 计算生物学
- 人工智能的人工智能
- 统计力学 统计力学
背景情况:
- 深度学习分类器提供准确的预测,但缺乏不确定性估计和功能可解释性.
- 扩散模型在数据生成方面表现出色,但在分类和预测任务中未得到充分利用.
研究的目的:
- 开发一个以物理学为灵感的框架,Keeping SCORE,它增强了用于概率分类和回归的扩散模型.
- 在不改变现有模型的情况下,使AI预测中的不确定性量化和特征归属成为可能.
主要方法:
- 通过测量沿噪声轨迹的消散,将扩散模型转化为概率引擎.
- 根据物理原理计算精确的类概率并量化基于物理原理的预测信心.
- 生成特征属性以识别关键输入变量驱动模型决策.
主要成果:
- 在各种任务中展示了准确的概率估计和信心量化,包括图像识别,单细胞基因组学和分子生物物理学.
- 提供可解释的特征属性,与具有物理意义的坐标相关联.
- 展示了框架在不修改预先训练的模型的情况下增强预测的能力.
结论:
- 保持SCORE桥梁非平衡统计力学和AI,创造可解释的,不确定性意识的预测.
- 这种方法通过对复杂数据集提供更深入的见解,推动了生物发现.
- 该框架为科学中可靠和可解释的AI应用提供了一个强大的工具.
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