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Updated: Jan 11, 2026

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渐变感知建模推进了人工智能驱动的基因扰动效应预测
1Department of Genetics, Stanford University, Stanford, 94305, CA, U.S.A.
bioRxiv : the preprint server for biology
|November 19, 2025
概括
我们开发了GARM,这是一个机器学习框架,用于预测基因干扰导致的基因表达变化. GARM提高了预测准确度,并为功能基因组学研究引入了更好的评估指标.
科学领域:
- 功能性基因组学是一种功能性基因组学.
- 机器学习 机器学习
- 计算生物学是一种计算生物学.
背景情况:
- 预测遗传干扰的转录效应至关重要,但具有挑战性.
- 像Perturb-seq这样的单细胞扰动测定产生了有价值的数据,但详尽的分析是不可行的.
- 预测模型的开发对于推进功能基因组学的发展至关重要.
研究的目的:
- 介绍GARM (渐变对齐回归与多解码器),一个新的机器学习框架.
- 提高对遗传干扰的转录反应的预测.
- 为扰乱预测模型建立严格的评估标准.
主要方法:
- 开发了GARM,一种使用渐变感知监控的机器学习框架.
- 利用多解码器架构来捕捉绝对和相对扰动效应.
- 引入了扰乱排名标准 (PrtR) 以改进模型评估.
主要成果:
- 在预测对未见扰动的反应方面,GARM始终优于现有方法 (GEARS,scGPT,GenePert).
- 证明了常见的评估指标高估了模型性能.
- 确定了基因特异性可预测性,突出了具有不同预测难度的通路和基因类.
结论:
- 在功能基因组学中,GARM为改善扰乱建模提供了一个统一的框架.
- 新的评估标准 (PrtR) 提供了对模型实用性的更现实的评估.
- 对基因特异性可预测性的洞察力为未来的模型开发和生物解释提供了信息.
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