Advanced Survival Transformer for Integrating Multi-Omics Longitudinal Data with High-Dimensional Fe

Music City Center 

The TransformerPseudo model, an advanced survival Transformer, is specifically designed to analyze high-dimensional, longitudinal multi-omics data while addressing the challenges of covariate-dependent censoring. By transforming survival outcomes into pseudo probabilities, the model circumvents the need for observed survival times and censoring variables, enabling robust estimation of covariate effects on patient survival. Its architecture leverages positional encodings and multi-head attention mechanisms to efficiently capture temporal dependencies and high-dimensional feature interactions, reducing information loss and simplifying the analysis by eliminating the reliance on random effects. Furthermore, the model employs SHAP values for interpretable visualization, offering comprehensive insights into the impact of multi-omics variables. Validated through extensive simulations and real-world applications using the TEDDY disease datasets, the TransformerPseudo model consistently achieves superior predictive accuracy and outperforms traditional methodologies.

Survival Analysis

Transformer

Deep Learning

Covariate-dependent censoring

High-dimensional longitudinal multi-omics

Robust estimation 

Section on Statistical Learning and Data Science