Sai Phani Parsa

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

This paper introduces BioExPL, a model-agnostic framework that integrates curated genetic biomarkers into neural networks to improve predictive performance and model interpretability.

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

This paper introduces BIN-PU, a PU learning framework for predicting bacterial protein-compound interactions using only positive samples, showing improved performance over existing methods.