BAREB：一种用于牙周数据的贝叶斯排斥双聚类模型
BAREB: A Bayesian repulsive biclustering model for periodontal data
摘要：
预防牙周病（PD）和维护牙齿的结构和功能是个人口腔护理的重要目标。为了了解具有不同PD模式的患者的异质性，我们开发了BAREB，这是一种贝叶斯排斥双聚类方法，可以在考虑患者和部位水平的协变量后同时对PD患者及其牙齿部位进行聚类。BAREB使用决定点过程（DPP）之前，诱导不同的双聚类之间的多样性，以促进简约性和可解释性。由于PD进展被假设为空间参考，BAREB因素在牙齿部位之间的空间依赖性。此外，由于PD是牙齿缺失的主要原因，因此缺失数据机制是不可解释的。这种非随机缺失被纳入BAREB。对于后验推理，我们设计了一个高效的可逆跳马尔可夫链蒙特卡罗采样器。仿真研究表明，BAREB是能够准确地估计双聚类，并比较有利的替代品。对于现实世界的应用，我们将BAREB应用于临床PD研究的数据集，并获得了理想的和可解释的结果。本文的一个主要贡献是BAREB的Rcpp实现，可在https://github.com/YanxunXu/上获得。
英文摘要：
Preventing periodontal diseases (PD) and maintaining the structure and function of teeth are important goals for personal oral care. To understand the heterogeneity in patients with diverse PD patterns, we develop BAREB, a Bayesian repulsive biclustering method that can simultaneously cluster the PD patients and their tooth sites after taking the patient- and site- level covariates into consideration. BAREB uses the determinantal point process (DPP) prior to induce diversity among different biclusters to facilitate parsimony and interpretability. Since PD progression is hypothesized to be spatially-referenced, BAREB factors in the spatial dependence among tooth sites. In addition, since PD is the leading cause for tooth loss, the missing data mechanism is non-ignorable. Such nonrandom missingness is incorporated into BAREB. For the posterior inference, we design an efficient reversible jump Markov chain Monte Carlo sampler. Simulation studies show that BAREB is able to accurately estimate the biclusters, and compares favorably to alternatives. For real world application, we apply BAREB to a dataset from a clinical PD study, and obtain desirable and interpretable results. A major contribution of this paper is the Rcpp implementation of BAREB, available at https://github.com/YanxunXu/ BAREB.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
