ReviewBriefings in bioinformatics2022
Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.
Review in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Oncogenic DNMT3B as a therapeutic target for cervical cancer.Chinese medical journal · 2026Article
- A novel prognostic biomarker combiningOncology letters · 2026Article
- Identifying Ovarian Cancer-Associated EV mRNA Expression Profiles Using Unsupervised Machine Learning and Non-Negative Matrix Factorization.Bioengineering (Basel, Switzerland) · 2026Article
- Multi-phase hybrid metabolomics framework identifies clinically applicable plasma signatures for early detection of gastric cancer.Nature communications · 2026Article
- Recovering missing features in nonnegative matrix factorization via generalized singular value decomposition.iScience · 2026Article
- Redefining the immune microenvironment of gliomas in the era of single-cell genomics.Neuro-oncology advances · 2026Review
- Current landscape of single-cell genomics in meningioma.Neuro-oncology advances · 2026Review
- Machine learning-based identification of extracellular matrix-related prognostic subtypes in SHH-activated medulloblastoma.Discover oncology · 2026Article
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- Fairer non-negative matrix factorization.Frontiers in big data · 2026Article
- Molecular Subtypes of Mixed Gastric Cancer Defined by Machine Learning for Predicting Prognosis and Treatment Response.Current medicinal chemistry · 2026Article
- Machine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications.International journal of molecular sciences · 2025Review
- SEPAR enables spatial metagene discovery and associated molecular pattern characterization in spatial transcriptomics and multi-omics datasets.Communications biology · 2025Article
- Mechanisms of synergistic regulation of the tumor microenvironment by fibroblasts and keratinocytes in esophageal squamous cell carcinoma.Discover oncology · 2025Article
- Leveraging diverse cell-death patterns to predict to predict prognosis and immunotherapy in hepatocellular carcinoma.Discover oncology · 2025Article
- LncRNAs regulates cell death in osteosarcoma.Scientific reports · 2025Article
- SPP1+ tumor-associated macrophages define a high-risk subgroup and inform personalized therapy in hepatocellular carcinoma.Frontiers in oncology · 2025Article
- Artificial intelligence in traditional Chinese medicine: advances in multi-metabolite multi-target interaction modeling.Frontiers in pharmacology · 2025Review
- Mitochondria-related genes as prognostic signature of endometrial cancer and the effect of MACC1 on tumor cells.PloS one · 2025Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.