Euiseong Ko

PINT: Pathway-pathway interactions for predicting interpretable clinical outcomes from gene expression

PINT predicts clinical outcomes from gene expression by modeling pathway-pathway interactions, outperforming benchmarks and revealing biologically meaningful pathway relationships.

Fairness-aware Supervised Hierarchical Contrastive Semantic Learning for Sexual Dimorphism Analysis

This paper introduces FairHICON, a fairness-aware supervised hierarchical contrastive learning method that identifies sex-specific and common genomic drivers, improving predictive performance and reducing sex-based performance gaps in cancer and asthma datasets.

PIMO: Pathway-based Interpretable Multi-Omics interactions for multi-omics integration

PIMO improves multi-omics integration by modeling pathway-based interactions, enhancing survival analysis and interpretability in cancer datasets.

Population-Scale Integration of Spatial Omics Networks for Clinical Prediction and Biological Discovery by SPIN

SPIN integrates spatial omics data for population-scale clinical prediction and biological discovery, achieving high accuracy in dementia and breast cancer outcomes.