在使用条件分数分布的残留水平上进行校准变异效应预测
Gal Passi1, Sapir Amittai1, Dina Schneidman-Duhovny1
1The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
bioRxiv : the preprint server for biology
|December 15, 2025
概括
我们为变异效应预测 (VEP) 模型引入了残留水平校准. 这种有针对性的方法改善了概率估计,提高了模型准确性,从而带来了更好的临床应用.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 机器学习在基因组学中的应用
背景情况:
- 准确和精确校准的变异效应预测 (VEP) 模型对于有效的临床使用至关重要.
- 当前的VEP模型往往缺乏可靠的概率估计,阻碍了它们的实际应用.
- 全球或每种蛋白质校准方案不能充分解决特定变异子组内的校准错误.
研究的目的:
- 开发一种实用且可靠的方法,用于在残留水平上校准VEP模型.
- 为了确定需要针对性校准以提高VEP性能的变体子组.
- 为了提高VEP预测在各种变体类型的可解释性和可靠性.
主要方法:
- 提出了一种残留水平校准策略,与全球或每蛋白质方法形成对比.
- 开发了RaCoon (通过有条件分布实现残余感知校准),在ESM1b模型上实施.
- 分析了特定模型的特征分布,以指导校准策略.
主要成果:
- 确定了特定的变体子组,其中VEP模型表现出显著的校准错误,尽管平均而言校准良好.
- RaCoon在各种变体子组中展示了多校准和可解释的预测.
- 在多个基准指标中实现了显著的绩效改进,将AUCROC从0.912提高到0.924.
结论:
- 有针对性的残留水平校准对于强大的VEP模型性能和可靠性至关重要.
- RaCoon提供了一种可转移和有效的策略,用于提高VEP校准和准确性.
- 开发的方法通过提供更有意义的变异效应概率估计来提高临床效用.
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