Zhaleh Safikhani

The paper introduces a cloud-based platform called ORCESTRA for reproducible processing and sharing of multimodal biomedical data in a FAIR manner.

The paper develops reproducible pharmacogenomic analysis pipelines using Common Workflow Language to process breast cancer datasets and share them with a Digital Object Identifier.

The paper identifies gene isoform-based biomarkers predictive of drug response in breast cancer using pharmacogenomic data from CCLE and GDSC.

PharmacoDB integrates large pharmacogenomic studies to enable comparison and analysis of drug screening data by curating identifiers and standardizing drug response metrics.

This paper reanalyzes pharmacogenomic data from GDSC and CCLE, finding continued inconsistency in drug sensitivity measures despite improved statistical methods and expanded datasets.

This study examines tissue-specific drug sensitivity in in vitro datasets and finds strong associations between tissue origin and drug response, but most preclinical findings do not align with clinical observations.

This paper introduces DNF, a computational framework that classifies new drugs by integrating structural, perturbation, and sensitivity data to infer drug relationships and taxonomies.

The paper reanalyzes pharmacogenomic data from GDSC and CCLE, finding continued inconsistency in drug response measures despite claims of agreement.

The paper compares drug response prediction models using cell lines, patients, and integrated data, finding that integrative models outperform cell line-only models and require fewer patients.