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Updated: Feb 5, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Published on: July 22, 2025

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PGVDA:一种通过路径聚合的基因剂量框架,用于使用机器学习进行可解释的疾病分类.

Sanghyun Shon1, Younhee Ko2, Hojin Yoon1,3

  • 1Department of Biomedical Informatics, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea.

Briefings in bioinformatics
|February 3, 2026
PubMed
概括

本研究引入了一种机器学习方法,使用基于途径的遗传变异剂量平均值 (PGVDA) 来通过分析生物途径内的遗传变异来区分神经肌肉结合障碍 (NMD) 和炎症性多神经病变 (IPN).

关键词:
遗传差异化基因差异化炎症性多神经病变 - 炎症性多神经病变机器学习分类机器学习分类.神经肌肉结合障碍 神经肌肉结合障碍基于路径的聚合.在SHAP分析中,我们分析了SHAP.

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