OmicsTransformer: self-supervised masked consistency and uncertainty-aware fusion for robust multi-omics prediction

OmicFormer: a statistical priors-informed transformer for accurate and generalizable omics prediction of diseases and complex traits

UMCA-Net: Uncertainty-aware multi-stage cross-attention for cost-aware multi-omics data classification.

Multi-omics data integration holds great promise for precision medicine, yet its clinical adoption is hindered by high acquisition costs and the complexity of heterogeneous data representations. To address these challenges, we propose an uncertainty-aware multi-view dynamic decision framework for efficient and trustworthy disease classification. Unlike conventional static fusion strategies, our approach leverages evidential deep learning grounded in Dempster-Shafer theory to explicitly disentangle predictive confidence from epistemic uncertainty, enabling cost-sensitive and progressive inference. Specifically, omics modalities are introduced adaptively, such that additional data are only acquired when the current evidence is insufficient to support a reliable decision. At the core of the UMCA-Net, a Transformer-based multi-stream architecture with global joint cross-attention captures rich cross-modal interactions and produces Dirichlet-based evidential representations. This design allows principled uncertainty quantification and supports dynamic decision-making. We evaluate the proposed method on four benchmark multi-omics datasets (ROSMAP, LGG, BRCA, and KIPAN). Experimental results demonstrate that our model achieves state-of-the-art performance while significantly reducing data acquisition requirements. Notably, in certain cohorts, over 90% of samples can be confidently classified using only low-cost initial modalities without compromising accuracy. Overall, this work provides a scalable and practical solution for balancing diagnostic accuracy and economic cost, facilitating the deployment of multi-omics models in real-world clinical settings. Our code is available to the public at github.com/chenzhao2023/UMCA-Net.

Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer

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.

A Markov Blanket-based framework for dependency-aware feature selection in multimodal breast cancer data

FastMI-HGNet: A Two-Stream Heterogeneous Graph Neural Network for Multi-Omics Disease Classification

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.

Self-Attention over Parallel Dense Embeddings for High-Dimensional Omic Data

High-dimensional omic datasets present major challenges for machine learning due to their sparse biological signal, strong feature heterogeneity, and high dimensionality. In this work, we propose PLAT (Parallel Latent Attention Transformer), a neural architecture for high-dimensional tabular transcriptomic data. The model projects input gene expression features into multiple parallel latent representations, each processed independently through self-attention to capture complementary feature interactions while maintaining moderate model complexity. The proposed architecture was evaluated using both controlled Negative Binomial simulations designed to reproduce RNA-seq overdispersion and the TCGA-BRCA breast cancer dataset comprising 499 patients and 4376 gene expression variables for ER+/ER− classification. Comparative analyses against a baseline multilayer perceptron and a lightweight FT-Transformer showed that PLAT achieves competitive predictive performance while maintaining a comparable number of trainable parameters. Simulation experiments further indicate that its main advantage is concentrated in specific high-dimensional settings with an intermediate proportion of informative features. To assess model interpretability, we additionally performed a SHAP-based analysis of the baseline MLP and compared it with the attention-derived gene rankings. Although both models identified largely different sets of predictive genes, functional enrichment analyses consistently highlighted biological processes and disease pathways associated with breast cancer, supporting the biological relevance of the learned latent representations. These results suggest that PLAT provides an effective and interpretable framework for high-dimensional transcriptomic classification.