Petr Smirnov

This study shows that p53 deficiency causes hypertranscription and nucleotide insufficiency, leading to replication stress and genomic instability, including chromothripsis.

This paper introduces PeML, a similarity metric learning method for perturbational datasets, improving the identification of perturbation mechanisms and biological relationships.

The paper assesses the inter-lab consistency of circular RNA expression in cancer cell lines and identifies factors affecting reproducibility.

PharmacoDB 2.0 enhances pharmacogenomics analysis by integrating new datasets, implementing FAIR pipelines, and improving drug response analysis tools.

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

This paper provides guidelines for developing machine learning models to predict drug sensitivity using cell line-based pharmacogenomics data, emphasizing proper training and validation practices.

This study develops RadioGx, a computational platform to analyze radioresponse in radiogenomic databases, identifying molecular pathways and drug correlations with radiation response.

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 develops Xeva, an open-source tool for analyzing PDX pharmacogenomic data, finding that few genes and pathways show passage-specific changes.

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.