Ghita Ghislat

Predicting temozolomide response in low-grade glioma patients with large-scale machine learning

This study uses machine learning on multi-omics data to predict temozolomide response in low-grade glioma patients, finding that miRNA-based models outperform conventional biomarkers.

A Pan-Cancer, Pan-Treatment (PCPT) Model For Predicting Drug Responses From Patient-Derived Xenografts With Application To Cancer Types With Sparse Training Data

The paper develops a pan-cancer, pan-treatment model to predict drug responses using patient-derived xenografts, showing it works well across cancer types with limited training data.

Machine learning models to predict in vivo drug response via optimal dimensionality reduction of tumour molecular profiles

The paper uses machine learning to predict drug response in tumour models by reducing molecular profile dimensionality, finding multi-gene predictors outperform single-gene markers in several cancer types.