{"doi":"10.1039/c9mo00162j","title":"Modular within and between score for drug response prediction in cancer cell lines","abstract":"<jats:title/>\n                  <jats:p>Drug response prediction in cancer cell lines is vital to discover new anticancer drugs. However, it's still a challenging task to accurately predict drug responses in cancer cell lines. In this study, we presented a novel computational approach, named as MSDRP (modular within and between score for drug response prediction), to predict drug responses in cell lines. The method is based on a constructed heterogeneous drug–cell line network with multiple information. Compared with other state-of-the-art methods, MSDRP acquired better predictive performance, and identified potential associations between drugs and cell lines, which have been confirmed by the published literature. The source code of MSDRP is freely available at https://github.com/shimingwang1994/MSDRP.git.</jats:p>\n                  <jats:p/>","journal":"Molecular Omics","year":2020,"id":592542,"datarank":0.45728804823420965,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"self_citation_contribution":0.31191623125197543,"citation_network_contribution":0.1453718169822342,"self_endowment_contribution":0.31191623125197543,"citer_contribution":0.1453718169822342,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":5,"citers_with_citation_signal":3,"citers_with_endowment":3,"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":235837,"name":"Jie Li","orcid":"0000-0002-2435-9646","position":1,"is_corresponding":false},{"id":1516276,"name":"Shiming Wang","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Modular within and between score for drug response prediction in cancer cell lines","abstract":"<jats:title/>\n                  <jats:p>Drug response prediction in cancer cell lines is vital to discover new anticancer drugs. However, it's still a challenging task to accurately predict drug responses in cancer cell lines. In this study, we presented a novel computational approach, named as MSDRP (modular within and between score for drug response prediction), to predict drug responses in cell lines. The method is based on a constructed heterogeneous drug–cell line network with multiple information. Compared with other state-of-the-art methods, MSDRP acquired better predictive performance, and identified potential associations between drugs and cell lines, which have been confirmed by the published literature. The source code of MSDRP is freely available at https://github.com/shimingwang1994/MSDRP.git.</jats:p>\n                  <jats:p/>","is_dataset_classified":null,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"31802092","pmcid":null,"openalex_id":"https://openalex.org/W2990045538","authors":[],"funders":[{"funder_name":"Natural Science Foundation of Heilongjiang Province","grant_id":"F2016016","title":null},{"funder_name":"National Basic Research Program of China","grant_id":"2016YFC0901905","title":null}],"total_grants":2,"fwci":0.7105,"citation_percentile":0.74385084,"influential_citations":0,"citation_trend":[{"year":2021,"count":3},{"year":2022,"count":1},{"year":2023,"count":1},{"year":2024,"count":1},{"year":2025,"count":1}],"oa_status":"closed","license":"http://rsc.li/journals-terms-of-use","oa_locations":[{"url":"https://academic.oup.com/molecular-omics/article-pdf/16/1/31/65034047/c9mo00162j.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1039/c9mo00162j","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/31802092","host_type":"repository"}],"fields_of_study":["Computational Drug Discovery Methods","vaccines and immunoinformatics approaches","Protein Structure and Dynamics","Algorithms","Antineoplastic Agents","Cell Line, Tumor","Computational Biology","Gene Expression Profiling","Gene Expression Regulation, Neoplastic","Gene Regulatory Networks","Humans","Internet","Models, Genetic","Neoplasms"],"mesh_terms":["Algorithms","Antineoplastic Agents","Humans","Models, Genetic","Neoplasms","Gene Expression Regulation, Neoplastic","Computational Biology","Internet","Gene Expression Profiling","Cell Line, Tumor","Gene Regulatory Networks"],"keywords":["Drug response","Cancer cell lines","Modular design","Drug","Cancer drugs","Cancer","Cell culture","Computer science","Medicine","Oncology","Cancer cell","Pharmacology","Biology","Internal medicine"],"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":"2026-07-26T14:08:24.605981Z","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":[]}