{"doi":"10.1016/j.nbt.2025.07.006","title":"Biology-aware machine learning for culture medium optimization","abstract":null,"journal":"New Biotechnology","year":2025,"id":653685,"datarank":0.29188652235829704,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.0,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"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":192885,"name":"Bei-Wen Ying","orcid":"0000-0003-2517-5686","position":1,"is_corresponding":false},{"id":1705595,"name":"Takamasa Hashizume","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Biology-aware machine learning for culture medium optimization","abstract":"Cell culture technologies are widely used in academia and industry, yet optimizing culture media remains an art due to the complexity of cell-medium interactions. Machine learning has emerged as a promising solution, but it is hindered by biological fluctuations and experimental errors. To address these issues, we developed a medium optimization platform that integrates simplified and effective experimental manipulation, error-aware data processing for model training, predictive model construction to enhance accuracy and avoid local optimization, and an efficient optimization framework of active learning. Using this approach, we fine-tuned a 57-component serum-free medium for CHO-K1 cells, in which a total of 364 media were experimentally tested. The reformulated medium achieved approximately 60 % higher cell concentration than commercial alternatives. The improved cell culture is definitive toward CHO-K1, underscoring the platform's precision in targeted cell culture optimization. Our approach offers a robust tool for optimizing complex systems in cell culture and broader experimental studies, as well as in biomedical engineering applications. • Build an ML-guided platform for optimizing cell culture media, explicitly accounting for biological variability and experimental noise. • Use biology-aware active learning to overcome limitations of traditional ML in biological experiments. • Reformulate a complex 57-component serum-free medium for human cell culture. • Provide a robust framework that bridges computational modeling and wet-lab experimentation.","is_dataset_classified":null,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"40716669","pmcid":null,"openalex_id":"https://openalex.org/W4412714070","authors":[],"funders":[{"funder_name":"Japan Society for the Promotion of Science","grant_id":"21K19815","title":null},{"funder_name":"Japan Society for the Promotion of Science","grant_id":"25K22838","title":null},{"funder_name":"Japan Society for the Promotion of Science","grant_id":"JP25KJ0680","title":null},{"funder_name":"RIKEN","grant_id":"","title":null}],"total_grants":4,"fwci":1.9183,"citation_percentile":0.86077604,"influential_citations":0,"citation_trend":[{"year":2025,"count":2},{"year":2026,"count":4}],"oa_status":"gold","license":"cc-by-nc-nd","oa_locations":[{"url":"https://doi.org/10.1016/j.nbt.2025.07.006","host_type":"journal"},{"url":"https://doi.org/10.1016/j.nbt.2025.07.006","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S1871678425000731?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S1871678425000731?httpAccept=text/plain","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/40716669","host_type":"repository"}],"fields_of_study":["Machine Learning in Materials Science","Cell Image Analysis Techniques","Innovative Microfluidic and Catalytic Techniques Innovation","Machine Learning","CHO Cells","Cricetulus","Animals","Cell Culture Techniques","Culture Media","Cricetinae"],"mesh_terms":["Machine Learning","Animals","Cricetulus","Culture Media","Cricetinae","CHO Cells","Cell Culture Techniques"],"keywords":["Computer science","Computational biology","Artificial intelligence","Biochemical engineering","Chemistry","Biology","Engineering","Machine Learning","Active Learning","medium optimization","Serum-free","Experimental Error","Biological Fluctuation","Error-aware Data Processing"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-10T23:51:19.909634Z","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":[]}