Supplementary Table 1: Unsupervised learning of cross-modal mappings in multi-omics data for survival stratification of gastric cancer

Abstract

Purpose: This study presents a survival-stratification model based on muti-omics integration using BiDNNs in GC.

Methods: Based on the survival-related representation features yielded by BiDNNs through integrating transcriptomics and epigenomics data, K-means clustering analysis was performed to cluster tumor samples into different survival subgroups. The BiDNNs-based model was validated using 10-fold cross-validation and in two independent confirmation cohorts.

Results: Using the BiDNNs-based survival stratification model, patients were grouped into two survival subgroups with log-rank P value=9.05E-05. The subgroups classification was robustly validated in 10-fold cross-validation (C-index=0.65±0.02) and in two confirmation cohorts (E-GEOD-26253, C-index=0.609; E-GEOD-62254, C-index=0.706).

Conclusion: We propose and validate a robust and stable BiDNNs-based survival stratification model in GC.