与SCID相关的遗传变异的基于多类机器学习的分类
Ali Şahin1, Gamze Sonmez2, Mehmet Karaselek3
1Department of Emergency Medicine, Dr. Vefa Tanır Ilgın State Hospital, Konya, 42600, Türkiye.
机器学习模型可以改善严重联合免疫缺陷 (SCID) 中不确定的意义 (VUS) 变异的解释. 随机森林模型在分类SCID相关的遗传变异方面表现出高准确性.
科学领域:
- 遗传学和基因组学 遗传学和基因组学
- 计算生物学 计算生物学
- 免疫学 免疫学 免疫学
背景情况:
- 不确定意义的变异 (VUS) 在基因检测中对免疫的先天性错误 (如严重联合免疫缺陷 (SCID)) 构成诊断挑战.
- 现有的计算工具显示出不一致性,通常需要昂贵的湿实验室验证.
研究的目的:
- 开发和评估特定于疾病的多类机器学习模型,用于分类与SCID相关的遗传变异.
- 为了提高VUS在SCID遗传学的解释.
主要方法:
- 鉴定了SCID基因,并从ClinVar中检索了误解变异,不包括VUS和相互矛盾的解释.
- 在 silico 收集了变体的功能预测得分.
- 开发和评估了6个机器学习模型 (随机森林,XGBoost,梯度提升,AdaBoost,SVM,物流回归) 使用5倍交叉验证.
主要成果:
- 分析了来自71个SCID相关基因的537个变异的数据集.
- 随机森林模型获得了最高的性能 (精度:0.70 ± 0.03,AUROC:0.90 ± 0.01).
- 确定了MetaRNN,BayesDel_addAF和REVEL作为最具预测性的特征.
结论:
- 特定于疾病的多类机器学习模型有效支持SCID变异分类.
- 这些模型为改善基因测试中的VUS解释提供了一个有希望的方法.
- 随机森林模型表现出卓越的诊断准确性和稳定性.
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