Krishna C. Bulusu

The paper identifies bimodal protein expression patterns in cancer proteomics, revealing 2401 tumor-associated bimodal proteins and their associations with key cancer pathways, with implications for biomarker discovery and precision oncology.

BEACON predicts drug side effects and therapeutic outcomes by integrating biomedical knowledge graphs with contextual language models, achieving high accuracy in drug sensitivity and drug-drug interaction predictions.

DDI-GPT predicts drug-drug interactions using a large language model enhanced with knowledge graphs, achieving high accuracy and providing explainable insights into interaction mechanisms.

PAIRWISE predicts effective personalized drug combinations in cancer using deep learning and transcriptome data, achieving high AUROC scores in validation tests.

The paper introduces a network biology-driven simulation method to predict effective drug combinations and identify biomarkers for DNA Damage Response inhibitors using cancer cell avatars.

The paper integrates knowledge graphs into machine learning models to improve survival prediction and biomarker discovery in non-small-cell lung cancer patients, showing significant improvements in hazard ratios and overall survival differentiation.

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.