预测遗传性癌症使用神经网络
By Zoe Guan1, Giovanni Parmigiani2, Danielle Braun3
1Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center.
The annals of applied statistics
|October 24, 2023
概括
神经网络可以比传统的孟德尔模型更准确地预测遗传癌症风险,即使是不完美的家族史数据. 这些先进的模型可以更好地识别患有遗传性癌症高风险的个体.
科学领域:
- 遗传学 遗传学 是一个
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 家庭病史是各种癌症的重要风险因素.
- 门德尔风险预测模型虽然有用,但依赖于可能难以验证的假设.
- 需要更灵活的模型来分析复杂的家族史数据.
研究的目的:
- 开发和评估一个用于将神经网络应用于癌症风险预测的家族史数据的框架.
- 研究神经网络学习遗传癌症易感性的能力.
- 将神经网络的性能与传统的孟德尔模型进行比较.
主要方法:
- 完全连接和卷积神经网络的适应被提议用于谱系数据.
- 模型被训练和测试在模拟数据的门德尔遗传.
- 绩效在一个大数据集 (风险服务队列) 上进行评估,并使用癌症遗传学网络数据进行验证.
主要成果:
- 神经网络模型在模拟的孟德尔遗传数据上实现了近乎最佳的预测性能.
- 当家族史数据包含错误报告的诊断时,神经网络的表现优于BRCAPRO孟德尔模型.
- 在风险服务队列中训练的模型预测了未来的乳腺癌风险.
结论:
- 神经网络提供了一个有希望的,灵活的方法来分析家族史数据来预测癌症风险.
- 这些模型表现出强度,即使有杂或不完整的家族史信息.
- 这些发现表明,有潜力改善癌症查和预防高风险个体的识别.
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