Wanru Guo

Younger breast cancer patients have too few samples to train reliable prognostic models, so LSAT-EN treats older patients as a source cohort and sets the strength of borrowing toward the source coefficients separately for each of six omics layers, while elastic-net regularization selects variables. In TCGA-BRCA, with patients aged 60 and older as source and those under 50 as target, four-year binary survival prediction reached an AUC of 0.819, above 0.774 for pooled fitting, 0.757 for target-only fitting, 0.728 for glmtrans and 0.780 for GAS-EN. In simulation the advantage grew as source-target differences and target-sample scarcity increased.

Younger breast cancer patients have too few samples to train reliable prognostic models, so LSAT-EN treats older patients as a source cohort and sets the strength of borrowing toward the source coefficients separately for each of six omics layers, while elastic-net regularization selects variables. In TCGA-BRCA, with patients aged 60 and older as source and those under 50 as target, four-year binary survival prediction reached an AUC of 0.819, above 0.774 for pooled fitting, 0.757 for target-only fitting, 0.728 for glmtrans and 0.780 for GAS-EN. In simulation the advantage grew as source-target differences and target-sample scarcity increased.