DepMicroDiff: Diffusion-Based Dependency-Aware Multimodal Imputation for Microbiome Data

• URL: http://arxiv.org/abs/2507.23676v1
• Date: Thu, 31 Jul 2025 15:51:41 GMT
• Title: DepMicroDiff: Diffusion-Based Dependency-Aware Multimodal Imputation for Microbiome Data
• Authors: Rabeya Tus Sadia, Qiang Cheng,
• Abstract summary: DepMicroDiff is a novel framework that captures the complex interdependencies between microbial taxa and contextual metadata that can inform imputation.<n>It substantially outperforms state-of-the-art baselines, achieving higher Pearson correlation (up to 0.712), cosine similarity (up to 0.812), and lower RMSE and MAE across multiple cancer types.
• Score: 13.110156202816112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Microbiome data analysis is essential for understanding host health and disease, yet its inherent sparsity and noise pose major challenges for accurate imputation, hindering downstream tasks such as biomarker discovery. Existing imputation methods, including recent diffusion-based models, often fail to capture the complex interdependencies between microbial taxa and overlook contextual metadata that can inform imputation. We introduce DepMicroDiff, a novel framework that combines diffusion-based generative modeling with a Dependency-Aware Transformer (DAT) to explicitly capture both mutual pairwise dependencies and autoregressive relationships. DepMicroDiff is further enhanced by VAE-based pretraining across diverse cancer datasets and conditioning on patient metadata encoded via a large language model (LLM). Experiments on TCGA microbiome datasets show that DepMicroDiff substantially outperforms state-of-the-art baselines, achieving higher Pearson correlation (up to 0.712), cosine similarity (up to 0.812), and lower RMSE and MAE across multiple cancer types, demonstrating its robustness and generalizability for microbiome imputation.

Related papers

• Cross-Sequence Semi-Supervised Learning for Multi-Parametric MRI-Based Visual Pathway Delineation [18.101169568060786]
  We propose a novel semi-supervised multi-parametric feature decomposition framework for VP delineation.<n>Specifically, a correlation-constrained feature decomposition (CFD) is designed to handle the complex cross-sequence relationships.<n>We validate our framework using two public datasets, and one in-house Multi-Shell Diffusion MRI (MDM) dataset.
  arXiv  Detail & Related papers  (2025-05-26T09:18:58Z)
• PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model [76.95536611263356]
  PolSAR data presents unique challenges due to its rich and complex characteristics.<n>Existing data representations, such as complex-valued data, polarimetric features, and amplitude images, are widely used.<n>Most feature extraction networks for PolSAR are small, limiting their ability to capture features effectively.<n>We propose the Polarimetric Scattering Mechanism-Informed SAM (PolSAM), an enhanced Segment Anything Model (SAM) that integrates domain-specific scattering characteristics and a novel prompt generation strategy.
  arXiv  Detail & Related papers  (2024-12-17T09:59:53Z)
• Pretrained-Guided Conditional Diffusion Models for Microbiome Data Analysis [1.433758865948252]
  We introduce mbVDiT, a novel pre-trained conditional diffusion model for microbiome data imputation and denoising. It 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.
  arXiv  Detail & Related papers  (2024-08-10T01:54:06Z)
• DiffPuter: Empowering Diffusion Models for Missing Data Imputation [56.48119008663155]
  This paper introduces DiffPuter, a tailored diffusion model combined with the Expectation-Maximization (EM) algorithm for missing data imputation.<n>Our theoretical analysis shows that DiffPuter's training step corresponds to the maximum likelihood estimation of data density.<n>Our experiments show that DiffPuter achieves an average improvement of 6.94% in MAE and 4.78% in RMSE compared to the most competitive existing method.
  arXiv  Detail & Related papers  (2024-05-31T08:35:56Z)
• FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival [3.4686401890974197]
  We propose a new end-to-end framework, FORESEE, for robustly predicting patient survival by mining multimodal information. Cross-fusion transformer effectively utilizes features at the cellular level, tissue level, and tumor heterogeneity level to correlate prognosis. The hybrid attention encoder (HAE) uses the denoising contextual attention module to obtain the contextual relationship features. We also propose an asymmetrically masked triplet masked autoencoder to reconstruct lost information within modalities.
  arXiv  Detail & Related papers  (2024-05-13T12:39:08Z)
• DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era [3.2549142515720044]
  This paper introduces a new process model for multimodal Data Fusion for Data Mining. Our model aims to decrease computational costs, complexity, and bias while improving efficiency and reliability. We demonstrate its efficacy through three use cases: predicting diabetic retinopathy using retinal images and patient metadata, domestic violence prediction employing satellite imagery, internet, and census data, and identifying clinical and demographic features from radiography images and clinical notes.
  arXiv  Detail & Related papers  (2024-04-18T15:52:42Z)
• Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities [9.476402318365446]
  In this work, we introduce a novel FL architecture designed to accommodate not only the heterogeneity of data samples, but also the inherent heterogeneity/non-uniformity of data modalities across institutions. We propose a solution by devising a distributed gradient blending and proximity-aware client weighting strategy tailored for multi-modal FL.
  arXiv  Detail & Related papers  (2024-01-07T23:45:01Z)
• Convolutional Monge Mapping Normalization for learning on sleep data [63.22081662149488]
  We propose a new method called Convolutional Monge Mapping Normalization (CMMN) CMMN consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data. Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture.
  arXiv  Detail & Related papers  (2023-05-30T08:24:01Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Deep neural networks approach to microbial colony detection -- a comparative analysis [52.77024349608834]
  This study investigates the performance of three deep learning approaches for object detection on the AGAR dataset. The achieved results may serve as a benchmark for future experiments.
  arXiv  Detail & Related papers  (2021-08-23T12:06:00Z)
• Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity [77.78624443410216]
  We propose improved FDAM algorithms for detecting heterogeneous chest data. A result of this paper is that the communication of the proposed algorithm is strongly independent of the number of machines and also independent of the accuracy level. Experiments have demonstrated the effectiveness of our FDAM algorithm on benchmark datasets and on medical chest Xray images from different organizations.
  arXiv  Detail & Related papers  (2021-02-09T04:05:19Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.