{"doi":"10.1093/bib/bbab260","title":"oncoPredict: an R package for predicting <i>in vivo</i> or cancer patient drug response and biomarkers from cell line screening data","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>Cell line drug screening datasets can be utilized for a range of different drug discovery applications from drug biomarker discovery to building translational models of drug response. Previously, we described three separate methodologies to (1) correct for general levels of drug sensitivity to enable drug-specific biomarker discovery, (2) predict clinical drug response in patients and (3) associate these predictions with clinical features to perform in vivo drug biomarker discovery. Here, we unite and update these methodologies into one R package (oncoPredict) to facilitate the development and adoption of these tools. This new OncoPredict R package can be applied to various in vitro and in vivo contexts for drug and biomarker discovery.</jats:p>","journal":"Briefings in Bioinformatics","year":2021,"id":46735,"datarank":10.220503059731223,"base_score":7.478169694159785,"endowment":7.478169694159785,"self_citation_contribution":1.1217254541239678,"citation_network_contribution":9.098777605607255,"self_endowment_contribution":1.1217254541239678,"citer_contribution":9.098777605607255,"corpus_percentile":null,"corpus_rank":null,"citation_count":1768,"citer_count":200,"citers_with_citation_signal":200,"citers_with_endowment":200,"datacite_reuse_total":25,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":216276,"name":"Robert F Gruener","orcid":null,"position":1,"is_corresponding":false},{"id":12358,"name":"R. Stephanie Huang","orcid":"0000-0002-9862-0368","position":2,"is_corresponding":false},{"id":216275,"name":"Danielle Maeser","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"oncoPredict: an R package for predicting <i>in vivo</i> or cancer patient drug response and biomarkers from cell line screening data","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>Cell line drug screening datasets can be utilized for a range of different drug discovery applications from drug biomarker discovery to building translational models of drug response. Previously, we described three separate methodologies to (1) correct for general levels of drug sensitivity to enable drug-specific biomarker discovery, (2) predict clinical drug response in patients and (3) associate these predictions with clinical features to perform in vivo drug biomarker discovery. Here, we unite and update these methodologies into one R package (oncoPredict) to facilitate the development and adoption of these tools. This new OncoPredict R package can be applied to various in vitro and in vivo contexts for drug and biomarker discovery.</jats:p>","is_dataset_classified":null,"base_score":7.478169694159785,"endowment":7.478169694159785,"datacite_reuse_total":25,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"34260682","pmcid":"PMC8574972","openalex_id":"https://openalex.org/W3182293965","authors":[],"funders":[{"funder_name":"National Institute of Health","grant_id":"R01CA204856","title":null},{"funder_name":"National Cancer Institute","grant_id":"R01CA229618","title":null},{"funder_name":"National Institutes of Health","grant_id":"5R01CA229618-05","title":"Genetic mechanisms underlying sexual dimorphism in cancer and response to therapy"},{"funder_name":"National Institutes of Health","grant_id":"5R01CA204856-05","title":"Drug repurposing in breast cancer"}],"total_grants":4,"fwci":258.9226,"citation_percentile":0.99993875,"influential_citations":125,"citation_trend":[{"year":2022,"count":96},{"year":2023,"count":329},{"year":2024,"count":575},{"year":2025,"count":534},{"year":2026,"count":234}],"oa_status":"green","license":"OUP Standard Publication Reuse","oa_locations":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8574972","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8574972","host_type":"GREEN"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8574972","host_type":"repository"},{"url":"https://academic.oup.com/bib/article-pdf/22/6/bbab260/41088373/bbab260.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1093/bib/bbab260","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/34260682","host_type":"repository"},{"url":"https://dx.doi.org/10.1093/bib/bbab260","host_type":""}],"fields_of_study":["Cell Image Analysis Techniques","Computational Drug Discovery Methods","Single-cell and spatial transcriptomics","Computer Science","Medicine","0301 basic medicine","0303 health sciences","03 medical and health sciences","Antineoplastic Agents","Biomarkers, Tumor","Cell Line, Tumor","Humans","Software"],"mesh_terms":["Antineoplastic Agents","Humans","Software","Biomarkers, Tumor","Cell Line, Tumor"],"keywords":["Biomarker","Biomarker discovery","Drug discovery","Drug","Drug response","In vivo","Cancer cell lines","Drug development","Computational biology","Computer science","Anticancer drug","Medicine","Bioinformatics","Cancer","Pharmacology","Biology","Internal medicine","Proteomics","Cancer cell","Biomarker Identification","Drug Response Prediction","Cell Line, Tumor","Biomarkers, Tumor","Humans","Antineoplastic Agents","Software"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. 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