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使用机器学习在未解决的继承性视网膜疾病队列中评估框架内Indel变体

David E Rauch^1,2, Meng Wang¹, Muhammad Jafar Hussain Hafiz¹

¹Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA, bcm.edu.

|March 4, 2026

概括

机器学习工具可以预测在遗传性视网膜疾病 (IRD) 中框架内插入和删除 (indels) 的致病性. MetaRNN-indel表现出卓越的性能,有助于识别IRD患者的因果变异.

科学领域:

遗传学是一个遗传学.
生物信息学是一种生物信息学.
眼科医生眼科眼科

背景情况:

预测框架内插入和删除 (indels) 的致病性是具有挑战性的,并且比其他遗传变异少标准化.
遗传性视网膜疾病 (IRD) 是一组多种影响视力的遗传性疾病,通常是由各种遗传突变引起的.

研究的目的:

系统地评估 in silico机器学习 (ML) 工具的性能,以预测 IRD 患者的内框架 indels 的致病性.
为了确定这种特定类型的遗传变异最有效的ML工具.

主要方法:

四个ML工具 (CADD,FATHMM-indel,VEST4和MetaRNN-indel) 使用患者队列与IRDs进行了比较.
性能最好的工具MetaRNN-indel被应用于一个更大的队列,1013个未解决的IRD患者.

主要成果:

在评估的ML工具中,MetaRNN-indel表现出最好的整体性能.
在1013名IRD患者中,MetaRNN-indel的应用确定了两种可能的致病性因果变异.
确定的变异被证实与各自患者的临床表型相关.

结论:

现有的ML工具,特别是MetaRNN-indel,可以可靠地预测框架内indels的病原性.
适当评估和调整ML工具对于在遗传疾病诊断中准确解释变异至关重要.
这项研究通过改进内部变异的分析来提高IRD的诊断能力.

关键词:

在IRD中,我们可以使用IRD.在框架内,indel.在-silicon-co. 在-silicon-co. 在-silico.机器学习是机器学习.没有解决的未解决问题.

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