转移学习以生存为基础的预测器聚类与应用到TP53突变注释
Xiaoqian Liu1, Hao Yan2, Haoming Shi3
1Department of Statistics, University of California at Riverside.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
一种新的方法,转移学习-生存基预测器集群 (TL-SCP),准确地注释了Li-Fraumeni综合征 (LFS) 患者的TP53突变. 这种方法改善了对TP53突变对生存的影响的理解,有助于临床管理.
科学领域:
- 遗传学和基因组学 在
- 计算生物学 计算生物学
- 癌症研究 癌症研究
背景情况:
- TP53突变在癌症中很常见,并导致Li-Fraumeni综合征 (LFS),这是一种遗传性癌症倾向.
- 准确的TP53突变注释对于管理LFS患者至关重要.
研究的目的:
- 开发一种新的计算方法,以基于其生存效应对TP53突变进行聚类.
- 增强突变注释,以改善LFS的临床解释性.
主要方法:
- 开发了基于生存的预测器集群 (SCP) 使用聚变惩罚的考克斯回归.
- 引入了TL-SCP,这是SCP的转移学习扩展,以利用外部数据提高性能.
- 利用加权排名平均来整合从源到目标数据集的排名信息.
主要成果:
- 在集群恢复和系数估计的模拟中,TL-SCP在SCP上表现优越.
- 在LFS患者中,TL-SCP成功地确定了生物学上有意义的TP53突变群.
- 与现有的注释相比,该方法提供了更好的临床解释性.
结论:
- 在LFS中,TL-SCP提供了一种可靠和可解释的TP53突变注释方法.
- 这种方法可以显著帮助临床管理和理解遗传性癌症综合征.
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