{"doi":"10.1016/j.csbj.2022.11.055","title":"scDrug: From single-cell RNA-seq to drug response prediction","abstract":null,"journal":"Computational and Structural Biotechnology Journal","year":2023,"id":589631,"datarank":1.2907182595010114,"base_score":3.9318256327243257,"endowment":3.9318256327243257,"self_citation_contribution":0.5897738449086489,"citation_network_contribution":0.7009444145923626,"self_endowment_contribution":0.5897738449086489,"citer_contribution":0.7009444145923626,"corpus_percentile":null,"corpus_rank":null,"citation_count":50,"citer_count":43,"citers_with_citation_signal":27,"citers_with_endowment":27,"datacite_reuse_total":0,"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":1508582,"name":"Jian-Hung Wen","orcid":null,"position":1,"is_corresponding":false},{"id":1508583,"name":"Shih-Ming Lin","orcid":null,"position":2,"is_corresponding":false},{"id":1508584,"name":"Tzu-Yang Tseng","orcid":null,"position":3,"is_corresponding":false},{"id":1508585,"name":"Jia-Hsin Huang","orcid":null,"position":4,"is_corresponding":false},{"id":1508586,"name":"Hsuan-Cheng Huang","orcid":"0000-0002-3386-0934","position":5,"is_corresponding":false},{"id":1508587,"name":"Hsueh-Fen Juan","orcid":"0000-0003-4876-3309","position":6,"is_corresponding":false},{"id":1508581,"name":"Chiao-Yu Hsieh","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"scDrug: From single-cell RNA-seq to drug response prediction","abstract":"Single-cell RNA sequencing (scRNA-seq) technology allows massively parallel characterization of thousands of cells at the transcriptome level. scRNA-seq is emerging as an important tool to investigate the cellular components and their interactions in the tumor microenvironment. scRNA-seq is also used to reveal the association between tumor microenvironmental patterns and clinical outcomes and to dissect cell-specific effects of drug treatment in complex tissues. Recent advances in scRNA-seq have driven the discovery of biomarkers in diseases and therapeutic targets. Although methods for prediction of drug response using gene expression of scRNA-seq data have been proposed, an integrated tool from scRNA-seq analysis to drug discovery is required. We present scDrug as a bioinformatics workflow that includes a one-step pipeline to generate cell clustering for scRNA-seq data and two methods to predict drug treatments. The scDrug pipeline consists of three main modules: scRNA-seq analysis for identification of tumor cell subpopulations, functional annotation of cellular subclusters, and prediction of drug responses. scDrug enables the exploration of scRNA-seq data readily and facilitates the drug repurposing process. scDrug is freely available on GitHub at https://github.com/ailabstw/scDrug.","is_dataset_classified":null,"base_score":3.9318256327243257,"endowment":3.9318256327243257,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"36544472","pmcid":"PMC9747355","openalex_id":"https://openalex.org/W4311769361","authors":[],"funders":[{"funder_name":"Ministry of Science and Technology, Taiwan","grant_id":"MOST 111-2321-B-002-017","title":null},{"funder_name":"Ministry of Education","grant_id":"NTU-111L8808","title":null},{"funder_name":"Ministry of Education","grant_id":"NTU-CC-109L104702-2","title":null},{"funder_name":"Ministry of Education","grant_id":"NTU-CC-111L893302","title":null}],"total_grants":4,"fwci":4.6225,"citation_percentile":0.96493705,"influential_citations":0,"citation_trend":[{"year":2023,"count":8},{"year":2024,"count":21},{"year":2025,"count":14},{"year":2026,"count":7}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.1016/j.csbj.2022.11.055","host_type":"journal"},{"url":"https://doi.org/10.1016/j.csbj.2022.11.055","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S2001037022005505?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S2001037022005505?httpAccept=text/plain","host_type":"publisher"},{"url":"https://spj.science.org/doi/pdf/10.1016/j.csbj.2022.11.055","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/36544472","host_type":"repository"},{"url":"https://doaj.org/article/f9238b3f09bd48209dd6cc3371a95b8e","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9747355","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC9747355","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC9747355?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Single-cell and spatial transcriptomics","Immune cells in cancer","Cancer Genomics and Diagnostics"],"mesh_terms":[],"keywords":["Computational biology","Workflow","Transcriptome","Pipeline (software)","Drug repositioning","RNA-Seq","Identification (biology)","Drug discovery","Drug","Biology","Computer science","Bioinformatics","Gene","Gene expression","Genetics","Database","Single-cell Rna-seq","Tumor Cell Subpopulations"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-24T00:49:16.996155Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}