Krishna C. Bulusu

Bimodality in pan-cancer proteomics reveals new opportunities for biomarker discovery

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: predicting side effects and therapeutics outcomes to drugs by Bridging knowlEdge grAph with CONtextual language model

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: Explainable Prediction of Drug-Drug Interactions using Large Language Models enhanced with Knowledge Graphs

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: Deep Learning-based Prediction of Effective Personalized Drug Combinations in Cancer

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

Integrating Knowledge Graphs into Machine Learning Models for Survival Prediction and Biomarker Discovery in Patients with Non-Small-Cell Lung Cancer

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.

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

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.