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使用AIGP进行自动解释的人工智能基因组预测

Lei Wei¹, Ziqin Jiang², Baoliang Fan³

¹China Agricultural University.

Genome research

|March 5, 2026

概括

机器学习模型,特别是增强算法,通过超越传统方法,显示出强大的基因组预测潜力. 整合生物见解和优化参数提高了准确性,新的工具提高了基因组研究的可访问性.

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:

从基因组突变中预测表型是遗传学的重大挑战.
像GBLUP和BayesR这样的传统方法在捕捉复杂的遗传效应方面存在局限性,包括表观症.
机器学习 (ML) 提供了一个强大的替代方案,但往往缺乏可解释性.

研究的目的:

将各种ML模型与各种农业物种基因组预测的传统方法进行评估.
确定影响基因组预测性能的关键因素,如特征架构和特征选择.
为基于机器学习的基因组预测研究开发一个可解释和可访问的工具包.

主要方法:

在真实和模拟数据集上评估了12个ML模型和两种传统方法 (GBLUP,BayesR).
评估了特征遗传架构和特征选择对预测性能的影响.
采用沙普利添加式解释 (SHAP) 来量化SNP效应,并开发了人工智能基因组预测 (AIGP) 工具包.

主要成果:

提升算法在评估的ML方法中表现出优越的性能.
特征遗传架构和特征选择被确定为预测准确性的主要决定因素.
考虑到基因相互作用效应和优化超参数显著提高了预测准确性.

结论:

机器学习,特别是增强算法,对推进基因组预测具有重大前景.
可解释的ML方法,先前生物信息的整合和参数优化对于最大限度地发挥ML的潜力至关重要.
AIGP工具包促进了自动化模型优化和可解释性,提高了ML对基因组选择的可访问性.

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