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Updated: Jul 9, 2026

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

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统一体质呼叫和基于机器学习的分类增强了CHIP的发现

Shulan Tian1, Garrett Jenkinson1, Alejandro Ferrer2

  • 1Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA.

Genomics, proteomics & bioinformatics
|April 29, 2025
PubMed
概括

一个名为UNISOM的新工作流提高了从标准测序数据中检测克隆血液形成 (CHIP) 突变的检测. 这种方法可以更好地识别小CHIP克隆,有助于对各种疾病的风险评估.

关键词:
不确定潜力的克隆性血液形成.机器学习 机器学习索马特变异调用索马特变异的调用整个前子序列的排序.整个基因组的测序.

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