利用高斯混合模型和贝叶斯规则改进患者分类和生物标志物评估。

Oncoscience Pub Date : 2019-12-23 eCollection Date: 2019-11-01 DOI:10.18632/oncoscience.494
Marina A Guvakova
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引用次数: 4

摘要

在临床研究中,确定测试结果中连续变量的临界值仍然具有挑战性,特别是在考虑候选生物标志物或疾病治疗靶点时。连续变量在两个种群中的分布被称为二分类,在临床研究中已被广泛使用。我们最近报道了一种确定连续变量的多个截止点的新方法。这种原始方法的发展是基于拟合高斯混合模型(GMM)到现实世界的临床数据。我们还探讨了如何利用贝叶斯概率来最小化不确定性,同时将个体患者分类到各自的亚群中。此外,我们还研究了所提出的方法在乳腺癌经典预后标志物分布中的性能。最后,我们应用该方法分析了候选标记物和癌症治疗靶点。在这里,我们提出了该方法的概述和我们的前景,其在生物医学和临床研究的实施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Improving patient classification and biomarker assessment using Gaussian Mixture Models and Bayes' rule.

Improving patient classification and biomarker assessment using Gaussian Mixture Models and Bayes' rule.

In clinical research, determining cutoff values for continuous variables in test results remains challenging, particularly when considering candidate biomarkers or therapeutic targets for disease. Distribution of a continuous variable into two populations is known as dichotomization and has been commonly used in clinical studies. We recently reported a new method for determining multiple cutoffs for continuous variables. The development of this original approach was based on fitting Gaussian Mixture Models (GMM) onto real-world clinical data. We also explored how to leverage Bayesian probability to minimize uncertainty while classifying individual patients into respective subpopulations. In addition, we investigated the performance of the proposed method for the distribution of classical prognostic markers in breast cancer. Finally, we applied the proposed method to analyze a candidate marker and a target for cancer therapy. Here, we present an overview of this method and our prospects for its implementation in biomedical and clinical research.

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