与可扩展,可解释的高斯过程学习序列-函数关系
bioRxiv : the preprint server for biology
|September 2, 2025
概括
我们开发了可解释的高斯过程模型来理解基因型-表型关系, 我们的方法提供了卓越的预测性能,
科学领域:
- 遗传学和生物信息学
- 计算生物学
- 系统生物学
背景情况:
- 了解基因型-表型关系在遗传学中至关重要,但由表观症 (上下文依赖的突变效应) 复杂化.
- 高通量表型生成大数据集,但标准模型难以普遍化和解释.
- 深度神经网络提供灵活性,但缺乏可解释性和不确定性量化.
研究的目的:
- 介绍一个可解释的序列函数关系的高斯过程模型的新型家族.
- 采用灵活的先前分布来捕捉体能景观模型.
- 为探索复杂的遗传相互作用提供可扩展和可解释的方法.
主要方法:
- 开发了可解释的高斯过程模型,具有灵活的先前分布以模型表达.
- 纳入位点,等位基因和突变特异性因素以量化表性影响.
- 使用GPU加速以扩展到大型数据集 (蛋白质,RNA,全基因组SNP).
主要成果:
- 在大型生物序列数据集上取得了卓越的预测性能.
- 产生可解释的模型参数,恢复已知的遗传特征.
- 发现了新的表观相互作用,为基因型-表型图提供了新的见解.
结论:
- 开发的高斯过程模型为研究序列-函数关系提供了可扩展和可解释的方法.
- 这些模型有效地捕捉了表观性,并提供了更深入的基因型-表型图.
- 这些方法适用于各种生物系统,包括DNA,RNA和蛋白质序列.
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