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一种结合LDA和神经网络的方法,用于预测抗瘤药物的疗效

Weiwei Zhu^1,2, Lei Zhang³, Xiaodong Jiang⁴

¹University of Science and Technology of China, Hefei, Anhui, China.

|September 11, 2024

概括

预测针对个性化癌症治疗的抗瘤药物疗效至关重要. 这项研究结合了隐性迪里克莱特分配 (LDA) 和神经网络,从临床数据准确预测药物反应,优于以前的方法.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

由于患者的遗传多样性,个性化医学和精确的癌症治疗越来越重要.
提前预测抗瘤药物的疗效仍然是有效的癌症治疗的重大挑战.

研究的目的:

开发一种方法,利用临床数据预测单个癌症患者的抗瘤药物疗效.
利用隐性迪里克莱特分配 (LDA) 和神经网络进行个性化药物反应预测.

主要方法:

来自癌症患者的临床文本数据被编码为概率分布向量,使用隐性迪里克莱特分配 (LDA) 模型.
一个神经网络被设计用于通过输入LDA表示来预测药物反应.
该模型在使用精度,回忆,F1得分,精度和ROC曲线下面面积 (AUC) 的金药物治疗的肺癌和肠癌患者数据上进行了评估.

主要成果:

拟议的方法实现了高性能指标,包括0.81的平均AUC,用于预测非小细胞肺癌患者中西斯的疗效.
对266名肠癌患者的独立数据集进行了性能验证,证明了可概括性.
与以前的方法相比,这种方法显示出更好的疗效预测,AUC至少高出4%.

结论:

结合LDA和神经网络,为从临床文本中预测抗瘤药物疗效提供了一个有希望的方法.
开发的方法显示了在临床实践中增强精确瘤治疗策略的潜力.

关键词:

药物疗效预测药物疗效预测在LDA模型中,LDA模型是临床大数据临床大数据神经网络的神经网络的神经网络文本采矿文本采矿是什么

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