DCMD：基于距离的微生物数据混合分布分类
DCMD: Distance-based Classification Using Mixture Distributions on Microbiome Data
摘要：
目前下一代测序技术的进展使研究人员能够对微生物组和人类疾病进行全面研究，最近的研究确定了人类微生物组与许多慢性疾病的健康结果之间的关联。然而，微生物组数据结构，其特征在于稀疏性和偏度，提出了挑战，建立有效的分类器。为了解决这个问题，我们提出了一种创新的方法，基于距离的分类使用混合分布（DCMD）。该方法旨在使用微生物群落数据时提高分类性能，其中预测因子由稀疏和异构计数数据组成。该方法通过估计样本数据的混合分布来模拟稀疏计数中的固有不确定性，并将每个观察值表示为分布，条件是观察到的计数和估计的混合物，然后将其用作基于距离的分类的输入。该方法被实施到一个k-means和k-近邻框架，我们确定两个距离度量，产生最佳的结果。使用模拟评估模型的性能，并将其应用于人类微生物组研究，将结果与许多现有的机器学习和基于距离的方法进行比较。所提出的方法是有竞争力的机器学习方法相比，并显示出明显的改善常用的基于距离的分类。适用性和鲁棒性的范围使所提出的方法成为使用稀疏微生物组计数数据进行分类的可行替代方案。
英文摘要：
Current advances in next generation sequencing techniques have allowed researchers to conduct comprehensive research on microbiome and human diseases, with recent studies identifying associations between human microbiome and health outcomes for a number of chronic conditions. However, microbiome data structure, characterized by sparsity and skewness, presents challenges to building effective classifiers. To address this, we present an innovative approach for distance-based classification using mixture distributions (DCMD). The method aims to improve classification performance when using microbiome community data, where the predictors are composed of sparse and heterogeneous count data. This approach models the inherent uncertainty in sparse counts by estimating a mixture distribution for the sample data, and representing each observation as a distribution, conditional on observed counts and the estimated mixture, which are then used as inputs for distance-based classification. The method is implemented into a k-means and k-nearest neighbours framework and we identify two distance metrics that produce optimal results. The performance of the model is assessed using simulations and applied to a human microbiome study, with results compared against a number of existing machine learning and distance-based approaches. The proposed method is competitive when compared to the machine learning approaches and showed a clear improvement over commonly used distance-based classifiers. The range of applicability and robustness make the proposed method a viable alternative for classification using sparse microbiome count data.
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。
