# Pretrained-Guided Conditional Diffusion Models for Microbiome Data Analysis

arXiv: https://arxiv.org/abs/2408.07709 · HTML: https://mlsanity.com/paper/2408.07709/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 10 Aug 2024.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 5 of 5)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [GraphMAE: Self-Supervised Masked Graph Autoencoders](https://mlsanity.com/paper/2205.10803/) (2022)

## Built on by (most cited first, 1 of 1)

- [DepMicroDiff: Diffusion-Based Dependency-Aware Multimodal Imputation for Microbiome Data](https://mlsanity.com/paper/2507.23676/) (2025)

## Abstract (arXiv)

Emerging evidence indicates that human cancers are intricately linked to human microbiomes, forming an inseparable connection. However, due to limited sample sizes and significant data loss during collection for various reasons, some machine learning methods have been proposed to address the issue of missing data. These methods have not fully utilized the known clinical information of patients to enhance the accuracy of data imputation. Therefore, we introduce mbVDiT, a novel pre-trained conditional diffusion model for microbiome data imputation and denoising, which uses the unmasked data and patient metadata as conditional guidance for imputating missing values. It is also uses VAE to integrate the the other public microbiome datasets to enhance model performance. The results on the microbiome datasets from three different cancer types demonstrate the performance of our methods in comparison with existing methods.