基于癌症依赖地图的可解释深度学习来识别癌症驱动基因
bioRxiv : the preprint server for biology
|July 14, 2025
概括
我们开发了新的深度学习模型,使用癌症依赖地图 (DepMap) 识别癌症驱动基因和突变. 这些工具提高了对瘤进展的理解,并有助于发现新的向治疗方法.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 癌症研究 癌症研究
背景情况:
- 识别癌症驱动基因和突变对于理解瘤进展和开发向疗法至关重要.
- 现有的方法在准确检测这些关键的遗传改变方面面临挑战.
研究的目的:
- 利用癌症依赖地图 (DepMap) 识别潜在的癌症驱动基因并推断多驱动器突变.
- 为癌症驱动基因发现和突变模式分析开发强大和可解释的深度学习模型.
主要方法:
- 开发了xNNDriver,这是一个监督的深度学习模型,将基因突变状态与全基因组依赖性得分联系起来.
- 使用了xAEDriver,这是一个无监督的可解释的自动编码器,可以同时推断多个驱动程序变体表示 (DVR).
- 应用模型用于癌症依赖地图 (DepMap) 数据集.
主要成果:
- xNNDriver成功地识别了既有驱动程序 (例如NRAS,KRAS,HRAS,BRAF) 和新候选者 (例如VDR).
- 该模型的可解释性揭示了由驱动基因调节的下游路径.
- 基于细胞系的xEADriver分层显示了不同的药物反应概况,表明了翻译潜力.
结论:
- 开发的框架为癌症驱动基因发现提供了强大的和可解释的方法.
- 这些模型为癌症生物学和突变模式提供了新的见解.
- 这项工作对推进向癌症治疗有重大影响.
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