Multi-omics molecular subtyping is complicated by small cohorts, heterogeneous data layers, and sensitivity to data splitting and feature selection. We developed multi-channel attention-based omics fusion (MCOF), an end-to-end framework that combines omics-specific projection, one-dimensional convolution, and integrated gradients for candidate biomarker prioritization. MCOF was evaluated against six baselines using five repeated stratified holdouts across four multi-omics datasets. In the restricted four-class breast invasive carcinoma task, MCOF achieved the highest observed mean for all five metrics, including an accuracy of 0.900 ± 0.022 and a macro-averaged area under the receiver operating characteristic curve (macro-AUC) of 0.982 ± 0.003. In the restricted three-class stomach adenocarcinoma task, it achieved the highest observed macro-F1 (0.786 ± 0.035), macro-AUC (0.934 ± 0.020), and weighted-AUC (0.930 ± 0.016), whereas support vector machines had a slightly higher accuracy and weighted F1. It also achieved the highest or near-highest AUC on the two non-cancer datasets. Compared with MOGONET, MCOF used 30.6% fewer trainable parameters on both cancer tasks. Sensitivity analyses indicated that the contribution of multi-omics fusion varied by dataset and metric. Excluding the 28 prediction analysis of microarray 50 (PAM50) genes present in the retained BRCA RNA panel produced only a modest reduction in the macro-AUC, from 0.982 ± 0.003 to 0.978 ± 0.004. Implementation tests across 20 trained checkpoints detected no batch-order effect or cross-sample gradient, while the IG rankings were moderately stable across repeated holdouts and numerically consistent across class-weighting schemes and 25–100 integration steps. MCOF provides a practical framework for multi-omics classification and candidate prioritization.