{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T03:50:45Z","timestamp":1776829845653,"version":"3.51.2"},"reference-count":146,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:00:00Z","timestamp":1656979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Advanced Intelligence Project","award":["JP21AC5001"],"award-info":[{"award-number":["JP21AC5001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.<\/jats:p>","DOI":"10.1093\/bib\/bbac246","type":"journal-article","created":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T05:27:15Z","timestamp":1656998835000},"source":"Crossref","is-referenced-by-count":63,"title":["Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2632-1334","authenticated-orcid":false,"given":"Ryuji","family":"Hamamoto","sequence":"first","affiliation":[{"name":"National Cancer Center Research Institute"}]},{"given":"Ken","family":"Takasawa","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project"}]},{"given":"Hidenori","family":"Machino","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence 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Process"},{"key":"2022071906185475600_ref46","first-page":"158","article-title":"GPU-accelerated non-negative matrix factorization for text mining","volume":"7337","author":"Kysenko","year":"2012","journal-title":"Nat Lang Process Inf Syst"},{"key":"2022071906185475600_ref47","article-title":"Co-sparse non-negative matrix factorization","volume":"15","author":"Wu","year":"2021","journal-title":"Front Neurosci"},{"key":"2022071906185475600_ref48","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1089\/heq.2021.0079","article-title":"A novel application of non-negative matrix factorization to the prediction of the health status of undocumented immigrants","volume":"5","author":"Li","year":"2021","journal-title":"Health Equity"},{"key":"2022071906185475600_ref49","article-title":"Indicator regularized non-negative matrix factorization method-based drug repurposing for COVID-19","volume":"11","author":"Tang","year":"2020","journal-title":"Front 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cancer","volume":"3","author":"Alexandrov","year":"2013","journal-title":"Cell Rep"},{"key":"2022071906185475600_ref56","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1016\/j.tig.2018.07.003","article-title":"Enter the matrix: factorization uncovers knowledge from omics","volume":"34","author":"Stein-O'Brien","year":"2018","journal-title":"Trends Genet"},{"key":"2022071906185475600_ref57","first-page":"617","article-title":"Self modeling curve resolution","volume":"13","author":"Lawton","year":"1971","journal-title":"Dent Tech"},{"key":"2022071906185475600_ref58","doi-asserted-by":"crossref","first-page":"S273","DOI":"10.1016\/S0021-8502(05)80089-8","article-title":"Matrix factorization methods for analysing diffusion battery data","volume":"22","author":"Paatero","year":"1991","journal-title":"J Aerosol Sci"},{"key":"2022071906185475600_ref59","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1002\/env.3170050203","article-title":"Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values","volume":"5","author":"Paatero","year":"1994","journal-title":"Environmetrics"},{"key":"2022071906185475600_ref60","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the parts of objects by non-negative matrix factorization","volume":"401","author":"Lee","year":"1999","journal-title":"Nature"},{"key":"2022071906185475600_ref61","first-page":"383","article-title":"Non-negative matrix factorization and its variants with applications to audio signal processing","volume":"44","author":"Kameoka","year":"2015","journal-title":"J Jpn Stat Soc"},{"key":"2022071906185475600_ref62","first-page":"556","article-title":"Algorithms for non-negative matrix factorization","volume":"13","author":"Lee","year":"2001","journal-title":"Adv Neural Inf Process Syst"},{"key":"2022071906185475600_ref63","first-page":"1457","article-title":"Non-negative matrix factorization with sparseness constraints","volume":"5","author":"Hoyer","year":"2004","journal-title":"J Mach Learn Res"},{"key":"2022071906185475600_ref64","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1200\/JCO.2010.28.5148","article-title":"Integrative genomic analysis of medulloblastoma identifies a molecular subgroup that drives poor clinical outcome","volume":"29","author":"Cho","year":"2011","journal-title":"J Clin Oncol"},{"key":"2022071906185475600_ref65","doi-asserted-by":"crossref","first-page":"4345","DOI":"10.1038\/onc.2015.499","article-title":"Enhanced MAPK signaling drives ETS1-mediated induction of miR-29b leading to downregulation of TET1 and changes in epigenetic modifications in a subset of lung SCC","volume":"35","author":"Taylor","year":"2016","journal-title":"Oncogene"},{"key":"2022071906185475600_ref66","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1158\/1078-0432.CCR-16-0140","article-title":"BRAF V600E mutant colorectal cancer subtypes based on gene expression","volume":"23","author":"Barras","year":"2017","journal-title":"Clin Cancer Res"},{"key":"2022071906185475600_ref67","article-title":"Identification and validation of immune molecular subtypes in pancreatic ductal adenocarcinoma: implications for prognosis and immunotherapy","volume":"12","author":"Li","year":"2021","journal-title":"Front Immunol"},{"key":"2022071906185475600_ref68","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1038\/nature12477","article-title":"Signatures of mutational processes in human cancer","volume":"500","author":"Alexandrov","year":"2013","journal-title":"Nature"},{"key":"2022071906185475600_ref69","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1126\/science.abo7425","article-title":"A fresh look at somatic mutations in 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