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Updated: Jan 9, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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CBKMR:基于Copula的贝叶斯内核机器回归框架,用于在Omics数据中进行最佳标记器检测

Anirban Chakraborty1, Chloe Mattila1, Debashis Ghosh2

  • 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.

bioRxiv : the preprint server for biology
|December 3, 2025
PubMed
概括

一种新的方法,基于copula的贝叶斯内核机器回归 (CBKMR),可以从omics数据中准确识别细胞类型标记. 这种方法处理复杂的基因相互作用和离散的结果,改进现有的贝叶斯内核机器回归 (BKMR) 和机器学习方法.

关键词:
贝叶斯的变量选择选择是贝叶斯的.批量产品有大量的消费.在 GKMR 中,GKMR 是斯的合体是高斯的合体.最接近的邻居GP近似值.这就是scRNA-seqq.

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