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在前列腺癌中利用机器学习进行严重程度水平智能生物标志物识别微阵列基因表达数据.

Ahmed Al Marouf1, Tarek A Bismar2,3,4,5,6, Sunita Ghosh7

  • 1Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada.

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概括

机器学习模型准确地识别了前列腺癌分级组的生物标志物,使用XGBoost.实现了96.85%的准确性. 这种方法有助于区分癌症的严重程度,以改善治疗指导.

关键词:
在XGBoost中使用.生物标记物识别识别方法前列腺癌是前列腺癌.组织微阵列.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 基因组学就是基因组学.
  • 在瘤学瘤学.

背景情况:

  • 前列腺癌是男性常见的癌症之一,因此精确检测和治疗至关重要.
  • 格里森分级组 (GGC) 评分对于确定前列腺癌的严重程度和指导治疗决策至关重要.

研究的目的:

  • 开发和验证一种机器学习 (ML) 框架,用于识别与前列腺癌中的不同格里森分级组 (GGC) 分数相关的潜在生物标志物.
  • 为了绘制前列腺癌的GGC得分,将其分为五个不同的严重程度:低,中等-低,中等,中等-高和高.

主要方法:

  • 使用传统的ML分类方法,包括决策树 (DT),随机森林 (RF),支持矢量机 (SVM) 和XGBoost (XGB).
  • 采用了一个框架,包括缺失值归算,SMOTE-Tomek链接用于类不平衡,和分层的k-fold交叉验证,以进行可靠的生物标志物选择.
  • 从1119个前列腺癌样本的免疫组织化学测试中获得的微阵列数据用于框架评估.

主要成果:

  • ML框架成功地区分了各种前列腺癌严重程度的关键生物标志物.
  • 在分类GGC分数方面,XGBoost方法实现了96.85%的高精度.
  • 使用高和低侵略性签名的组合的四个实验设置证明了该方法的有效性.

结论:

  • 机器学习为识别前列腺癌生物标志物提供了一个强大的平台,支持领域专家的参与.
  • 该研究的令人满意的结果表明,医生在循环中的方法有可能提高未来研究中的诊断影响.