Computational Techniques for Cancer Prognosis using Multi-Omics Data

This preprint presents computational techniques for cancer prognosis using the integration of multiple biological data modalities, including genomic, transcriptomic, methylation, and potentially proteomic information. The proposed framework addresses the challenges of heterogeneous data scales, high dimensionality, limited patient cohorts, and overfitting in multi-omics modelling. The methodology combines modality-specific preprocessing, variational multimodal encoding, shared latent representations, patient-similarity graph reasoning, and survival prediction using a Cox proportional hazards objective. Performance is evaluated against single-omics baselines using survival-analysis measures including concordance index, integrated Brier score, time-dependent AUC, Kaplan–Meier analysis, and log-rank testing. Interpretability techniques are also incorporated to identify biological signals contributing to prognosis predictions.