Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine. Issue 4 (5th July 2022)

• Record Type: Journal Article
• Authors: Hamamoto, Ryuji; Takasawa, Ken; Machino, Hidenori; Kobayashi, Kazuma; Takahashi, Satoshi; Bolatkan, Amina; Shinkai, Norio; Sakai, Akira; Aoyama, Rina; Yamada, Masayoshi; Asada, Ken; Komatsu, Masaaki; Okamoto, Koji; Kameoka, Hirokazu; Kaneko, Syuzo
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 4(2022)
• Publication Date: 2022-07-05
• Subjects: machine learning -- NMF -- omics analysis -- single-cell analysis -- meta-analysis; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac246
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: 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.