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联合学习用于在多个临床场所的临床环境中对遗传变异的病原性注释

Nigreisy Montalvo¹, Francisco Requena², Emidio Capriotti^3,4

¹Clinical Bioinformatics Laboratory, INSERM UMR1163, Imagine Institute, Université Paris Cité, Paris F-75006, France.

Bioinformatics (Oxford, England)

|September 19, 2025

概括

联合学习 (FL) 允许机器学习模型在不共享敏感患者数据的情况下进行基因变异分类的协作训练. 这种方法的准确性与传统方法相比或更高,改善了罕见疾病的诊断.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
机器学习机器学习

背景情况:

罕见疾病影响5%的人口,但超过一半的患者在全基因组测序后缺乏分子诊断.
目前用于变异性致病性的机器学习模型由于隐私和对访问机构数据集的法律限制而受到有限的培训数据的困扰.
联合学习 (FL) 提供了一个解决方案,通过在机构之间实现协作模式培训,而无需直接共享数据.

研究的目的:

评估联合学习 (FL) 策略对人类遗传变异临床分类的有效性.
将联合模型的性能与传统的集中式学习方法进行比较.
评估FL模型在独立数据集上的概括能力.

主要方法:

使用Python实现各种联合学习策略.
对编码和非编码单核酸变异 (SNV) 和复制数变异 (CNV) 的 FL 评估.
在外部队列中,将联合模型性能与集中式学习模型进行比较.

主要成果:

联合模型表现出与基因变异分类的集中模型相比的或更高的性能.
FL模型表现出对独立数据集的强有力的概括,即使数据分数较小.
该研究提供了FL在变体解释的安全多机构合作中的概念验证.

结论:

联合学习是一种可行和有效的方法,用于训练准确的遗传变异分类模型.
FL促进了机构之间的安全合作,克服了数据隐私障碍.
采用FL可以显著推进罕见疾病的分子诊断.

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