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基因驱动的分析学习模型用于准确的乳腺癌诊断

Farah Hesham^1,2, Mohammed M Abbassy³, Mohammed Abdalla³

¹Information Technology Program,The Egyptian-Korean Faculty of Technological Industry and Energy, Beni-Suef Technological University (BTU), Beni-Suef, Egypt. farahhisham472_sd@fcis.bsu.edu.eg.

Scientific reports

|March 3, 2026

概括

结合卷积神经网络 (CNN) 和双向长期短期记忆 (BiLSTM) 网络的新型深度学习模型使用基因表达数据改善了乳腺癌预后预测. 该工具为乳腺癌患者的精准医学提供了更高的准确性.

科学领域:

计算生物学计算生物学
生物信息学是一种生物信息学.
机器学习在瘤学中的应用

背景情况:

由于疾病的异质性,乳腺癌的预后有很大差异.
准确的预后预测对于个性化治疗策略至关重要.
基因表达数据具有改善乳腺癌预测结果的潜力.

研究的目的:

开发一个集成的深度学习模型,以改善乳腺癌的预后.
通过相关性分析确定一个强大的基因组用于预后预测.
验证模型在不同数据集中的性能和通用性.

主要方法:

开发了一种混合深度学习模型,集成卷积神经网络 (CNN) 和双向长期短期记忆 (BiLSTM) 网络.
利用皮尔森相关性从癌症基因组图谱-乳腺癌 (TCGA-BRCA) 数据中识别出236个基因组.
在TCGA-BRCA和METABRIC数据集上训练和验证模型,并使用Optuna贝叶斯优化进行优化.

主要成果:

混合CNN-BiLSTM模型显著超过现有的机器和深度学习方法.
实现了0.9943的召回,0.9955的ROC AUC和0.9962的F1得分,超过了仅使用BiLSTM模型的性能.
在20%的噪声干扰下,具有最小差异 (0.000083) 的统计学稳定性.

结论:

集成的CNN-BiLSTM深度学习框架为乳腺癌精确医学提供了强大的计算工具.
该模型从基因表达数据提供高度准确和一致的预后预测.
这种方法有可能改善临床决策和乳腺癌护理中的患者结果.

关键词:

乳腺癌的预后乳腺癌的预后外部验证的验证方法基因与基因的相互作用混合型CNN-BiLSTM可以使用.超参数调整超参数调整转录基因生物标志物

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