鉴定与乳腺癌患者生存相关的基因调节网络,使用可解释深度神经网络模型
Xue Wang1, Vivekananda Sarangi2, Daniel P Wickland1
1Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Rd. S., Jacksonville, FL, USA, 32224.
概括
一个新的深度神经网络MaskedNet准确地预测乳腺癌存活率,并识别IFNG等关键基因. 这种模型提供了生物学见解,将IFNG与免疫细胞存在和改善生存结果联系起来.
科学领域:
- 生物医学研究的研究.
- 计算生物学是一种计算生物学.
- 癌症基因组学 癌症基因组学
背景情况:
- 人工神经网络在生物医学研究中表现有前途,但在生存分析方面面临挑战.
- 优化模型的准确性和生物解释性对于临床实用性至关重要.
研究的目的:
- 开发一个深层神经网络 (MaskedNet),用于识别与乳腺癌患者生存相关的基因和途径.
- 从生存分析模型中增强生物见解.
主要方法:
- 开发了MaskedNet,这是一个在TCGA乳腺癌数据上训练的深度神经网络.
- 使用SHapley添加式解释 (SHAP) 解释模型输出来赋予特征重要性.
- 在一个独立的乳腺癌临床试验中验证的结果.
主要成果:
- 面具网的准确性高于传统的考克斯回归.
- 确定了IFNG和PIK3CA基因以及与整体存活相关的相关途径.
- 发现更高的IFNG SHAP值与更好的生存率相关,与M1巨细胞和T细胞透有关.
结论:
- 在癌症研究中,MaskedNet有效地将生存分析与生物解释性结合起来.
- IFNG途径与免疫微环境和生存的关联在一个独立的队列中得到了验证.
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