Title: Hierarchical Sparse Bayesian Multitask Model with Scalable Inference for Microbiome Analysis Show Chinese title

Title: 用于微生物组分析的分层稀疏贝叶斯多任务模型及其可扩展推理方法

Abstract: This paper proposes a hierarchical Bayesian multitask learning model that is applicable to the general multi-task binary classification learning problem where the model assumes a shared sparsity structure across different tasks. We derive a computationally efficient inference algorithm based on variational inference to approximate the posterior distribution. We demonstrate the potential of the new approach on various synthetic datasets and for predicting human health status based on microbiome profile. Our analysis incorporates data pooled from multiple microbiome studies, along with a comprehensive comparison with other benchmark methods. Results in synthetic datasets show that the proposed approach has superior support recovery property when the underlying regression coefficients share a common sparsity structure across different tasks. Our experiments on microbiome classification demonstrate the utility of the method in extracting informative taxa while providing well-calibrated predictions with uncertainty quantification and achieving competitive performance in terms of prediction metrics. Notably, despite the heterogeneity of the pooled datasets (e.g., different experimental objectives, laboratory setups, sequencing equipment, patient demographics), our method delivers robust results. Show Chinese abstract

Abstract: 本文提出了一种分层贝叶斯多任务学习模型，适用于一般的多任务二分类学习问题，该模型假设不同任务之间共享稀疏结构。 我们基于变分推理推导出一种计算效率高的推理算法以近似后验分布。 我们在各种合成数据集以及基于微生物组特征预测人类健康状况方面展示了新方法的潜力。 我们的分析结合了来自多个微生物组研究的数据，并与其他基准方法进行了全面比较。 合成数据集中的结果显示，当底层回归系数在不同任务间共享共同的稀疏结构时，所提出的模型具有优越的支持恢复特性。 我们在微生物组分类实验中证明了该方法在提取信息丰富的分类群方面的实用性，同时提供了经过良好校准的预测和不确定性量化，并在预测指标方面取得了具有竞争力的表现。 值得注意的是，尽管合并的数据集存在异质性（例如，不同的实验目标、实验室设置、测序设备、患者人口统计），我们的方法仍能提供稳健的结果。