在内核PCA中改进变量的解释性

Mitja Briscik1, Marie-Agnès Dillies2, Sébastien Déjean3

  • 1Institut de Mathématiques de Toulouse, UMR5219, CNRS, UPS, Université de Toulouse, Cedex 9, 31062, Toulouse, France. mitja.briscik@math.univ-toulouse.fr.

BMC bioinformatics
|July 12, 2023
PubMed
概括

本研究介绍了Kernel PCA可解释梯度 (KPCA-IG),这是一种快速的,数据驱动的方法,用于在高通量数据中的特征重要性. KPCA-IG准确地识别了有影响力的变量,可能会发现新的生物标志物.

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