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先进的机器学习框架,通过转录基因分析来增强乳腺癌诊断.

Mohamed J Saadh1, Hanan Hassan Ahmed2, Radhwan Abdul Kareem3

  • 1Faculty of Pharmacy, Middle East University, Amman, 11831, Jordan.

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概括

这项研究开发了用于乳腺癌诊断的机器学习 (ML) 框架,使用转录组数据和高级特征选择实现了高精度. 该模型显示了个性化癌症护理的前景.

关键词:
生物标志物 生物标志物乳腺癌 乳腺癌 乳腺癌选择功能选择功能选择.机器学习 机器学习预测建模的预测建模.转录形状分析 (Transcriptomic Profiling) 是一种方法,可以进行转录形状分析.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 机器学习在医疗保健中的应用

背景情况:

  • 准确的乳腺癌诊断对于有效治疗至关重要.
  • 整合转录基因数据为分子分析提供了一种强大的方法.
  • 机器学习模型可以提高诊断准确性和可解释性.

研究的目的:

  • 开发用于乳腺癌诊断的先进机器学习 (ML) 框架.
  • 将转录形状分析与优化特征选择和分类相结合.
  • 提高乳腺癌检测的诊断准确性和模型解释性.

主要方法:

  • 分析了1759个样本 (987个乳腺癌,772个对照) 使用特征选择 (递归特征消除,Boruta,ElasticNet).
  • 通过非负矩阵因子化 (NMF),自动编码器和变压器嵌入 (BioBERT,DNABERT) 来减少维度.
  • 培训和评估分类器 (XGBoost,LightGBM,集体投票) 的交叉验证和对175个样本的外部验证.

主要成果:

  • XGBoost和LightGBM实现了高测试准确度 (0.91,0.90) 和AUC (0.92),特别是在NMF和BioBERT.
  • 集体投票显示出优异的外部验证准确度 (0.92),表明了强度.
  • 变压器嵌入式和高级功能选择优于PCA等传统方法.

结论:

  • 拟议的ML框架显著提高了乳腺癌的诊断准确性和可解释性.
  • 该模型在独立的数据集上显示出强大的通用性.
  • 调查结果支持该框架在精确瘤学和个性化诊断方面的潜力.