Computational Biology meets AI Innovation

Next-Generation Microbiome × Bayesian Machine Learning

Pioneering next-generation biological computation through novel Bayesian machine learning methods. This breakthrough approach opens new pathways for applying Bayesian AI in medicine - from precise disease diagnosis to personalized treatment plans and accelerated drug development. By making complex microbiome data more interpretable, we're bringing AI-driven healthcare solutions closer to clinical reality.

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🧬 Bayesian AI × Microbiome Engine

Live Analysis

Bayesian Model

AI Pipeline

Results

# I-SVVS: Integrative Stochastic Variational Variable Selection
class BayesianMicrobiomeModel:
    def __init__(self):
        # Prior distributions
        self.alpha = Gamma(1.0, 1.0)  # DP concentration
        self.beta = Beta(1.0, 1.0)   # Stick-breaking
        self.tau = Gamma(0.1, 0.1)   # Precision
    def elbo(self, microbiome, metabolome):
        # Evidence Lower BOund optimization
        likelihood = log_p_x_theta(microbiome, metabolome)
        kl_div = KL(q_theta, p_theta)
        return likelihood - kl_div

Bayes' Theorem:
p(θ|X) = p(X|θ) × p(θ) / p(X)
posterior = likelihood × prior / evidence θ: microbiome parameters, X: multi-omics data

10x

ELBO Convergence

97%

Posterior Accuracy

50K+

Taxa Features

∞

Mixture Components

🧬

Complex Biology

50K+ Features

⚡

Bayesian AI

🎯

Clinical Insights

97% Accuracy

10x Faster Analysis

$M+ Cost Savings

∞ Scalability

                                        🧠
                                        Stochastic Variational Inference
                                    

                                        📊
                                        Multi-Omics Integration
                                    

Disease Prediction Model

🔄 Real-time Analysis

Microbiome-Disease Association Network

IF: 16.8

Microbiome Journal Publication

50x

Performance Improvement

Top-Tier Publications

$M+

Healthcare Cost Savings

Breakthrough Research

Pioneering AI-driven computational methods for next-generation biological discovery

Newest

VBayesMM: Variational Bayesian Microbiome Multiomics

Briefings in Bioinformatics 2025 Impact Factor: 8.7

Project Homepage | Press Release

Novel Features

Superior Accuracy Uncertainty Prediction Feature Selection Data Integration Massive Scalability Interpretation Forecasts

Data Types

Discrete Data Continuous Data Metabolomics Microbiome

Technical Solutions

Deep Learning Bayesian Neural Network Bayesian Supervised Learning Spike and Slab Priors Variational Inference TensorFlow PyTorch

I-SVVS: Integrative Stochastic Variational Variable Selection

Briefings in Bioinformatics 2025 Impact Factor: 8.7

Project Homepage | Press Release

Novel Features

Data Integration Clustering Accuracy Feature Selection Massive Scalability Interpretation Forecasts

Data Types

Discrete Data Continuous Data Metabolomics Microbiome

Technical Solutions

Hierarchical Dirichlet Process Stochastic Variational Inference Bayesian Unsupervised Learning Python

SVVS: Stochastic Variational Variable Selection

Microbiome 2022 Impact Factor: 16.837

Project Homepage

Novel Features

Clustering Accuracy Feature Selection Massive Scalability Interpretation Forecasts

Data Types

Discrete Data Microbiome

Technical Solutions

Dirichlet Process Stochastic Variational Inference Bayesian Unsupervised Learning Python

SVBPhylo: Variational Bayesian Phylogenetic Inference

Molecular Biology and Evolution 2019 Impact Factor: 14.797

Source Code

Novel Features

Clustering Accuracy Massive Scalability

Data Types

Discrete Data DNA/RNA Sequencing

Technical Solutions

Bayesian phylogenetics Model Stochastic Variational Inference Parallel Computing Massive Scalability HPC C++

oFVSD: optimized Forward Variable Selection Decoder

Frontiers in Neuroinformatics 2023

Source Code

Novel Features

Superior Accuracy Feature Selection

Data Types

Neuroimaging Structural MRI

Technical Solutions

Supervised Learning Forward variable selection Parallel Computing Python

Computational Biology meets AI Innovation

Disease Prediction Model

Professional Profile

Academic Website

Google Scholar

GitHub

ORCID

ResearchMap

Breakthrough Research

VBayesMM: Variational Bayesian Microbiome Multiomics

I-SVVS: Integrative Stochastic Variational Variable Selection

SVVS: Stochastic Variational Variable Selection

SVBPhylo: Variational Bayesian Phylogenetic Inference

oFVSD: optimized Forward Variable Selection Decoder

Publications

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

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

Stochastic Variational Variable Selection for High-Dimensional Microbiome Data

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

Technical Expertise

🧬 Computational Biology

🤖 AI & Machine Learning

💻 Programming & Computing

📊 Data Science & Statistics

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