Pretrained-Guided Conditional Diffusion Models for Microbiome Data Analysis
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910565969952768 |
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| author | Shi, Xinyuan Zhu, Fangfang Min, Wenwen |
| author_facet | Shi, Xinyuan Zhu, Fangfang Min, Wenwen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07709 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pretrained-Guided Conditional Diffusion Models for Microbiome Data Analysis Shi, Xinyuan Zhu, Fangfang Min, Wenwen Genomics Machine Learning 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. |
| title | Pretrained-Guided Conditional Diffusion Models for Microbiome Data Analysis |
| topic | Genomics Machine Learning |
| url | https://arxiv.org/abs/2408.07709 |