Tung Dang

QBayMic: Quantum-coupled variational Bayes for clustering and feature selection in low-signal microbiome data

QBayMic improves clustering and feature selection in low-signal microbiome data by using quantum-coupled variational Bayes, outperforming classical methods in stability and accuracy.

A hyperspherical deep Bayesian model for interpretable clustering and relationship prediction in microbiome multi-omics integration

DBayesCM integrates multi-omics microbiome data using a deep Bayesian model to enable interpretable clustering and relationship prediction across diseases.

VBayesMM: Variational Bayesian neural network to prioritize important relationships of high-dimensional microbiome multiomics data

VBayesMM improves identification of microbial-metabolite relationships in high-dimensional microbiome data by using a Bayesian neural network with spike-and-slab priors.

I-SVVS: Integrative stochastic variational variable selection to explore joint patterns of multi-omics microbiome data

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

oFVSD: A Python package of optimized forwardvariable selection decoder for high-dimensionalneuroimaging data

oFVSD improves ML decoding accuracy in neuroimaging by optimizing feature selection and using parallel computation.

Stochastic variational variable selection for high-dimensional microbiome data

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

Forward variable selection improves the power of random forest for high- dimensional microbiome data

The paper improves random forest's power for high-dimensional microbiome data by combining forward variable selection with random forest, achieving higher accuracy in case-control studies.

Stochastic Variational Inference for Bayesian Phylogenetics: A Case of CAT Model

The paper develops a variational Bayesian method to accelerate phylogenetic inference in the CAT model, achieving accurate results with significantly reduced computational time.