# Biomarker Discovery

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.

This dissertation integrates multimodal retinal imaging, including fundus photography, OCT, and OCTA, to build a suite of deep learning frameworks spanning vessel segmentation networks, biomarker discovery pipelines, and multimodal fusion models for detecting early glaucomatous changes with high precision, robustness, and interpretability, and contributes several curated datasets including GSS-RetVein as standardized benchmarks for cross-domain validation, reporting that the proposed models outperform existing state-of-the-art approaches across multiple public and clinical datasets with marked improvements in diagnostic sensitivity and specificity.

This dissertation integrates multimodal retinal imaging, including fundus photography, OCT, and OCTA, to build a suite of deep learning frameworks spanning vessel segmentation networks, biomarker discovery pipelines, and multimodal fusion models for detecting early glaucomatous changes with high precision, robustness, and interpretability, and contributes several curated datasets including GSS-RetVein as standardized benchmarks for cross-domain validation, reporting that the proposed models outperform existing state-of-the-art approaches across multiple public and clinical datasets with marked improvements in diagnostic sensitivity and specificity.