OmicsTransformer: Self-Supervised Masked Consistency and Uncertainty-Aware Fusion for Robust Multi-Omics Prediction

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ID: 319095
2026
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Abstract
Abstract Motivation Multi-omics integration can improve cancer diagnosis and prognosis, but current models are limited by extreme dimensionality, redundant raw-feature similarities, missing assays, and incomplete pathway priors. We ask whether biologically meaningful patient manifolds can be learned directly from high-dimensional multi-omics data without heuristic graph construction or fixed knowledge-base constraints. Results We present OmicsTransformer, an end-to-end framework that projects each omics modality into latent patches, enforces masked semantic consistency through an Exponential Cosine Consistency Loss, models global patch dependencies with a Transformer encoder, and fuses modalities by sample-specific uncertainty. Across eight diagnostic and prognostic cohorts, OmicsTransformer achieved strong performance, including 89.4% accuracy for TCGA-BRCA subtyping and 90.6% area under the receiver operating characteristic curve (AUC) for TCGA-LGG grading. It improved recurrence prediction over the pathway-restricted DeepKEGG baseline by approximately 21.5 percentage points in accuracy (ACC) on TCGA-LIHC and 11.1 percentage points in ACC on TCGA-BLCA. Variance-weighted attribution with ensemble stability selection recovered reproducible cross-modal biomarker cores and non-canonical progression drivers. Availability and Implementation Source code and datasets are freely available at https://github.com/FFJXX/OmicTransformer and https://doi.org/10.6084/m9.figshare.31523905. OmicsTransformer is implemented in PyTorch. Supplementary Information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7166588197 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors J. H. Feng, Boxuan Shan, Jie Deng, Zixin Jiang, Siqin Peng, Sijun Peng, Jian Yang, Gang Wang, Xiaogang Peng, Xiaozheng Li
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag468
URL
https://doi.org/10.1093/bioinformatics/btag468
Keywords Keywords not found

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