Bayesian Methods and Mixture Models

I-SVVS improves clustering accuracy and reduces computation time for multi-omics microbiome data across soybean, mice, and human datasets.

The paper introduces a clustering-based method to model dynamic accumulation processes in disease progression and antimicrobial resistance evolution using hypercubic hidden Markov models.

The paper introduces SARP models to cluster spatially resolved transcriptomics data and identifies co-localized immune and stromal cell subtypes in colorectal cancer.

The paper develops dependent mixture models to compare immune cell subtypes between early-onset and late-onset colorectal cancer, identifying distinct and shared T cell profiles.

The paper introduces a Bayesian model to infer RNA life-cycle kinetic rates from sequencing data and applies it to study Myc activation in murine fibroblasts, revealing new regulatory patterns.

This paper introduces DPMUnc, a Bayesian clustering method that incorporates data uncertainty, and applies it to GWAS and gene expression data to identify disease subgroups.

The paper evaluates how different assumptions in hidden Markov models affect state decomposition in electrophysiological data using synthetic and real data.

The paper introduces SVVS, a method for analyzing high-dimensional microbiome data, showing improved performance and faster computation compared to existing methods.

The paper introduces a consensus clustering method for Bayesian mixture models, showing it can uncover target clustering structures and handle large datasets efficiently.

MGMM fits Gaussian mixture models on incomplete data, outperforming imputation methods in recovering cluster assignments and assessing uncertainty.

The paper develops a tree-structured shrinkage method for latent class analysis that integrates sample similarities to improve class probability estimation.

This paper introduces a Bayesian nonparametric model to infer subclonal populations from structured DNA sequencing data, showing improved performance over existing methods.

The paper develops a Bayesian method for clustering multivariate binary data with scientific constraints, showing improved interpretability and clustering performance in protein data analysis.

CYBERTRACK is a statistical framework for clustering and tracking cell populations in time-series flow cytometry data, showing consistent results with known lymphocyte dynamics.

The paper introduces a semi-supervised clustering model for identifying active enhancers in mouse heart tissues using heterogeneous genomic data.

This paper introduces BERMUDA, a probabilistic model for analyzing microbial composition and disease, showing improved handling of zero-inflated data and providing efficient learning through an annealing EM algorithm.

The paper introduces a 2-way k-means model for clustering microbiome samples that can handle mixed compositions between clusters, applied to 16S rDNA sequencing data from the Human Vaginal Microbiome Project.