基于机器学习的预测人类的结构变化和相关序列决定因素的表征
Daven Lim1,2, Runyang Nicolas Lou3, Nilah Ioannidis4,5
1Department of Biosystems Science and Engineering, ETH Zürich, Zürich, Switzerland.
bioRxiv : the preprint server for biology
|December 19, 2025
概括
机器学习模型现在可以预测人类基因组中的结构变异 (SV) 形成. 这些模型识别出使区域易患SVs的序列特征,有助于理解遗传多样性和疾病.
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.
- 计算生物学 计算生物学
背景情况:
- 结构变异 (SV) 对遗传多样性,进化和人类疾病至关重要.
- 量化局部序列环境对SV形成的影响是具有挑战性的.
研究的目的:
- 开发机器学习模型,用于预测人类基因组中的SV发生.
- 识别导致SV形成的基因组决定因素.
主要方法:
- 开发了一个仅序列的卷积神经网络 (CNN) 模型.
- 使用随机森林方法整合基因组注释.
- 采用模型解释性技术来识别关键的基因组贡献者.
主要成果:
- 两种模型都实现了高预测性能 (>90%的AUROC),通过整体方法得到了改进.
- 确定了序列动图 (微同质,非正规的DNA结构) 和 SV热点作为关键决定因素.
- 不同的SV类 (可转移的元素,反转) 显示不同的序列签名.
- 预测的SV概率与等位基因频率和基因功能约束相关.
结论:
- 局部序列背景准确地预测了易受SV影响的基因组区域.
- 机器学习模型为量化SV易感性提供了一个框架.
- 研究结果支持这些模型在个性化基因组学中用于变异效应预测的实用性.
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