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.
Abstract
Integrating complex, multi-omics data presents significant challenges. Existing approaches often face a trade-off between model interpretability and representational capacity, with most either relying on post-hoc interpretation or use linear models that may overlook complex interactions. We report Pathway Activity Autoencoders for the multi-omics setting, which embed prior knowledge via pathway-informed architectural constraints, fostering interpretability, while preserving representational power. Our multi-omic framework is applied in the context of breast cancer and is evaluated in survival prediction and subtype classification with results indicating a positive effect of integration. We conduct analysis of individual omics layer impact on end-task performance, revealing that gene, protein, and microRNA expression layers provide the strongest contribution. Repeatability studies indicate that, while dropout improves model robustness and consistency, excessive regularisation can reduce predictive performance. Finally, visualizations of the learned feature space illustrate the framework's intrinsic transparency and clinical relevance. The results underscore the value of multi-omic integration and delineate the impact of individual omics layers, establishing practical guidelines for integration within our framework. Overall, our pathway activity autoencoder frameworks yield superior latent representations that are biologically meaningful and are directly translatable into clinically relevant insights.
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
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.
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
Background/Objectives: Multi-omics datasets are increasingly used for disease classification, but differences in scale, distribution, and resolution across omics layers complicate their integration. Conventional fusion approaches may overlook nonlinear cross-omics dependencies and structured sample–feature relationships. Here, we propose FastMI-HGNet, a two-stream heterogeneous graph neural network for multi-omics disease classification. Methods: The framework uses fast mutual information (FastMI) to construct dependency edge priors for a heterogeneous graph that connects sample and feature nodes. A Transformer-based data stream captures vector-level feature interactions, while a graph attention stream models structural dependencies among samples and molecular features. An uncertainty-aware ensemble further improves stability under small-sample and noisy multi-omics settings. Results: Evaluated on five public multi-omics benchmarks—ROSMAP, LGG, BRCA, and the more challenging COAD tumor-stage classification task, together with KIPAN as a ceiling-level proof-of-concept benchmark—FastMI-HGNet achieved competitive classification performance while supporting interpretable biomarker prioritization. In BRCA, SHAP-based analysis highlighted model-prioritized genes such as FOXC1 and SOX10. Conclusions: FastMI-HGNet supports interpretable multi-omics disease classification and biomarker prioritization.
Xufeng Fu, Bipeng Lai, Rong-Ling He et al.· Genes· 0 citations