{"doi":"10.1007/978-1-4939-8955-3_13","title":"Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing","abstract":null,"journal":"Methods in Molecular Biology","year":2019,"id":641655,"datarank":0.5570358100056463,"base_score":3.713572066704308,"endowment":3.713572066704308,"self_citation_contribution":0.5570358100056463,"citation_network_contribution":0.0,"self_endowment_contribution":0.5570358100056463,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":40,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"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":1668483,"name":"Hon-Cheong So","orcid":null,"position":1,"is_corresponding":false},{"id":17708,"name":"Kai Zhao","orcid":"0000-0003-0468-2128","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing","abstract":"The cost of new drug development has been increasing, and repurposing known medications for new indications serves as an important way to hasten drug discovery. One promising approach to drug repositioning is to take advantage of machine learning (ML) algorithms to learn patterns in biological data related to drugs and then link them up to the potential of treating specific diseases. Here we give an overview of the general principles and different types of ML algorithms, as well as common approaches to evaluating predictive performances, with reference to the application of ML algorithms to predict repurposing opportunities using drug expression data as features. We will highlight common issues and caveats when applying such models to repositioning. We also introduce resources of drug expression data and highlight recent studies employing such an approach to repositioning.","is_dataset_classified":null,"base_score":3.713572066704308,"endowment":3.713572066704308,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"30547445","pmcid":null,"openalex_id":"https://openalex.org/W2903687253","authors":[],"funders":[],"total_grants":0,"fwci":14.7052,"citation_percentile":0.98798014,"influential_citations":0,"citation_trend":[{"year":2018,"count":1},{"year":2019,"count":3},{"year":2020,"count":7},{"year":2021,"count":8},{"year":2022,"count":8},{"year":2023,"count":6},{"year":2024,"count":4},{"year":2025,"count":2},{"year":2026,"count":1}],"oa_status":"closed","license":"http://www.springer.com/tdm","oa_locations":[{"url":"http://link.springer.com/content/pdf/10.1007/978-1-4939-8955-3_13","host_type":"publisher"},{"url":"https://doi.org/10.1007/978-1-4939-8955-3_13","host_type":"book series"},{"url":"https://pubmed.ncbi.nlm.nih.gov/30547445","host_type":"repository"}],"fields_of_study":["Computational Drug Discovery Methods","Gene expression and cancer classification","Genetics, Bioinformatics, and Biomedical Research","Algorithms","Computational Biology","Databases, Pharmaceutical","Deep Learning","Drug Repositioning","Gene Expression Profiling","Gene Expression Regulation","Genomics","Humans","Machine Learning","Neural Networks, Computer","Reproducibility of Results","Supervised Machine Learning","Transcriptome","Workflow"],"mesh_terms":["Machine Learning","Supervised Machine Learning","Deep Learning","Algorithms","Gene Expression Regulation","Humans","Reproducibility of Results","Neural Networks, Computer","Computational Biology","Gene Expression Profiling","Genomics","Workflow","Drug Repositioning","Transcriptome","Databases, Pharmaceutical"],"keywords":["Drug repositioning","Repurposing","Computer science","Machine learning","Drug","Drug discovery","Artificial intelligence","Data science","Data mining","Bioinformatics","Pharmacology","Medicine","Engineering","Biology","Genomics","Deep Learning","Drug Transcriptome"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-07T19:31:46.700480Z","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":[]}