Aug 2026· Genomics, Proteomics & Bioinformatics· 0 citations
Medicine
TL;DR
A novel multi-modal deep learning model with intermediate fusion: multi-omics fusion neural network- computational cell counting (MOFUN-CCC) designed to predict absolute cell counts directly by integrating gene expression and DNA methylation data within a supervised framework, assuming that the underlying true cell components are shared across the two omics data.
Abstract
As the volume of omics data continues to grow exponentially, there is an increasing demand for innovative methodologies that combine multi-omics data to extract meaningful clinical insights. Absolute cell counts are a fundamental component of clinical evaluations for disease diagnosis, treatment, and patient management. While cellular deconvolution can estimate relative cell type proportions from bulk data, obtaining absolute cell counts from omics data remains rarely studied. In response to the clinical needs and challenges, we introduce a novel multi-modal deep learning model with intermediate fusion: multi-omics fusion neural network- computational cell counting (MOFUN-CCC). This model is designed to predict absolute cell counts directly by integrating gene expression and DNA methylation data within a supervised framework, assuming that the underlying true cell components are shared across the two omics data. Comprehensive evaluations, including cross-validation, independent data testing, and real-world applications, demonstrate the model's robustness, precision, and capacity to effectively capture biological variations. MOFUN-CCC represents a pioneering effort in the integration of multi-omics data for the prediction of absolute cell counts. With our user-friendly software (https://github.com/yuemolin/MOFUN-CCC) and web application (https://shiny.crc.pitt.edu/mofun_shiny/), this innovation holds the potential to make significant contributions to disease diagnosis, progression analysis, and clinical decision-making.
This work reports Pathway Activity Autoencoders for the multi-omics setting, which embed prior knowledge via pathway-informed architectural constraints, fostering interpretability, while preserving representational power, in the context of breast cancer.
Pedro Henrique da Costa Avelar, Ou-Yang Le, Min Wu et al.· 0 citations
Per-dataset analysis reveals three reproducible regimes: probabilistic variational autoencoder variants help on the smallest datasets, deep autoencoders win on mid-scale data with multi-batch or many-type structure, and classical PCA pipelines remain competitive when linear projection already captures the dominant variation.
N. Phong, T. Vu, Nguyen Ha Thu et al.· 0 citations
artificial intelligence (AI) is transforming the way we stumble on ailments early, but relying on a single form of facts—which includes genomics on my own—offers most effective a restrained picture of the whole organic complexity. Multi-omics integration—alongside aspect genomics, transcriptomics, proteomics, metabolomics, microbiome information, and scientific signs and symptoms and signs and symptoms—offers a more whole view of illness improvement. modern-day-day research show that graph neural networks (GNNs), federated getting to know (FL), and explainable AI (XAI) outperform genomic-most effective models through identifying novel biomarkers and enhancing diagnostic accuracy. Examples embody Tab net fusion for Alzheimer’s, multimodal deep reading for rheumatoid arthritis, and semi-supervised analyzing for hepatocellular carcinoma. By comparing genomic and multi-omics strategies, this survey highlights ongoing hurdles related to privacy, bias, interpretability, and scalability. destiny tips which consist of transformer-based fusion and basis fashions promise equitable, transparent, and clinically relevant AI-driven multi-omics structures for precision medicine.
Ahamadi Firdose, Deepthi Raj D, Bhargavi B, Jenita J, Apoorva H G, Dr. Madhu Gopinath· International Journal of Adv...· 0 citations
ProphDR is an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism, and generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes consistent with established mechanisms in NSCLC and BRCA.
Yundian Zeng, Qing Ye, Jike Wang et al.· Journal of Chemical Informat...· 0 citations
Disease classification using clinical and metabolomics data increasingly relies on multimodal integration, yet the complementary and comparative contributions of these modalities remain poorly understood. Most existing frameworks prioritize predictive performance without systematically examining how data modalities and modeling paradigms influence classification outcomes. Consequently, the relative value of individual modalities versus their integration, particularly in terms of model behavior, robustness, and interpretability, remains poorly characterized across machine learning (ML) and deep learning (DL) approaches. Here, we present a modality-aware comparative framework that enables direct, side-by-side evaluation of clinical-only, metabolomics-only, and combined modeling strategies within a unified pipeline. Unlike existing tools designed primarily for multiomics integration, this framework explicitly assesses when and how each modality contributes value across diverse classification scenarios. It supports the efficient use of available data while enabling a systematic comparison of model performance, stability, and interpretability across ML and DL methods. Rather than introducing a new classifier model, this work delivers a unified benchmarking workflow for evidence-based decision-making of modeling strategies in small-sample clinical metabolomics settings. Applied to two glomerulonephritis cohorts representing clinically-driven and metabolomics-driven classification settings, the framework revealed that the dominant contributing modality shifted by scenario, reflecting differences in disease context. These scenarios reflect realistic situations where discriminative signals arise from clinical variables, metabolic alterations, or their combination, as well as more complex cases involving subclass discrimination with overlapping profiles. While several models achieved comparable predictive accuracy, they differed in feature ranking stability, sampling sensitivity, and tendency to overfitting. Overall, this framework facilitates transparent, evidence-based selection of modeling strategies and data modalities suited to data complexity, sample size, and research objectives. Source code is available at https://github.com/kwanjeeraw/MMFramework.
K. Wanichthanarak, Kassaporn Duangkumpha, Nichapa Kleebkomut et al.· ACS Measurement Science Au· 0 citations
Advancements in multi-omics research have demonstrated the potential of integrating human microbiome and metabolomics data to better understand physiological processes and improve prediction accuracy in studies of human health. While conventional models utilizing single-omics data provide valuable perspectives, they often fail to capture the complexity of biological systems. Recent developments in supervised contrastive learning frameworks have enhanced predictive performance for categorical responses, yet limitations persist in extending these methods to continuous outcomes. A robust model capable of addressing these gaps could significantly enhance multi-omics predictions and provide new insights into complex biological interactions. Here, we present MB-SupCon-cont, a novel supervised contrastive learning framework designed for both categorical and continuous responses in multi-omics data. MB-SupCon-cont improves prediction accuracy by incorporating a generalized contrastive loss function that defines similarity and dissimilarity for continuous responses using three distance-based weighting methods. Through simulation studies and two real-world datasets for Type 2 Diabetes (T2D) and High-Fat Diet (HFD), we demonstrate that MB-SupCon-cont consistently achieves lower prediction errors than tuned conventional models, canonical correlation analysis, and autoencoder baselines, with most reaching statistical significance. We further provide a validation-based rule for selecting the weighting method and show that the learned embeddings align more closely with the response and recover known microbe and metabolite associations. The framework also provides superior representation learning and improves data visualization in lower-dimensional spaces. These findings suggest that MB-SupCon-cont is a powerful tool for general multi-omics prediction and may have broad applicability in biomedical research.
Sen Yang, Shidan Wang, Yiqing Wang et al.· Frontiers in microbiomes· 0 citations