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NN-PCP:查与表型相关的核心途径,以建立基于多种类型的突变数据的前列腺癌转移预测模型

Li Zhou¹, Jie Li²

¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150000, China.

Computer methods and programs in biomedicine

|October 18, 2025

概括

一个新的神经网络,NN-PCP,通过分析基因突变和通路,准确地预测前列腺癌转移. 这种工具有助于医生提高前列腺癌诊断准确度.

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:

前列腺癌是一个重大的全球健康问题,每年造成超过40万例死亡,并列为男性癌症相关死亡的第五大原因.
基于基因突变的前列腺癌转移的准确预测仍然是临床瘤学的关键挑战.

研究的目的:

开发一种新的深度学习模型,NN-PCP (神经网络与表型相关的核心途径),用于准确预测前列腺癌转移.
为了利用表型相关的核心途径和突变数据来增强诊断能力.

主要方法:

提出了一个新的神经网络,NN-PCP,整合了与表型相关的核心途径来预测转移.
利用IORA驱动的模块从突变数据中提取路径特征,并使用IGSEA驱动的模块将其转化为差异特征.
采用具有两个差异层的层次结构,以捕捉多种类型突变数据的协同效应.

主要成果:

与四种最先进的方法相比,NN-PCP表现优越,在准确性,精度,回忆,F1分数,AUC和AUPR方面显著改善.
确定了关键基因 (例如,PIK3R1,PIK3CA) 和通路 (例如,PI3K-AKT信号通路),对于前列腺癌转移性预测至关重要.
在精度方面取得了5.7%,精度方面7.8%,回忆方面1.2%的性能改进.

结论:

开发的NN-PCP方法超越了现有的基于途径的深度学习方法来预测前列腺癌转移.
对于临床医生来说,NN-PCP可以作为一种有价值的辅助工具,提高前列腺癌诊断的准确性.

关键词:

生物途径生物途径转移转移转移转移突变突变突变突变神经网络的神经网络的神经网络

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