基于机器学习的贝叶斯超参数优化eQTL分析

Andrew Quitadamo, James Johnson, Xinghua Shi
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引用次数: 3

摘要

机器学习方法被广泛应用于生物学和生物信息学的问题。这些方法通常依赖于配置高级参数或超参数,例如稀疏学习模型中的正则化超参数,如图引导的多任务Lasso方法。这些超参数的不同选择将导致不同的结果,这使得在使用这些超参数依赖方法时找到良好的超参数组合成为一项重要任务。有几种不同的方法可以调优超参数,包括手动调优、网格搜索、随机搜索和贝叶斯优化。在本文中,除了贝叶斯优化之外,我们还将网格和随机搜索三种超参数优化策略应用于eQTL分析。实验表明,贝叶斯优化策略在eQTL关联建模方面优于其他策略。利用gEUVADIS的1000个基因组结构变异基因型和RNAseq数据,应用该策略评估eQTL关联,我们确定了一组与人类群体中基因表达变化相关的新SVs。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian Hyperparameter Optimization for Machine Learning Based eQTL Analysis
Machine learning methods are being applied to a wide range of problems in biology and bioinformatics. These methods often rely on configuring high level parameters, or hyperparameters, such as regularization hyperparameters in sparse learning models like graph-guided multitask Lasso methods. Different choices for these hyperparameters will lead to different results, which makes finding good hyperparameter combinations an important task when using these hyperparameter dependent methods. There are several different ways to tune hyperparameters including manual tuning, grid search, random search, and Bayesian optimization. In this paper, we apply three hyperparameter tuning strategies to eQTL analysis including grid and random search in addition to Bayesian optimization. Experiments show that the Bayesian optimization strategy outperforms the other strategies in modeling eQTL associations. Applying this strategy to assess eQTL associations using the 1000 Genomes structural variation genotypes and RNAseq data in gEUVADIS, we identify a set of new SVs associated with gene expression changes in a human population.
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