ArticleBMC bioinformatics2013
Non-negative matrix factorization by maximizing correntropy for cancer clustering.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Robust Sparse Non-Negative Matrix Factorization for Identifying Signals of Interest in Bearing Fault Detection.Sensors (Basel, Switzerland) · 2025Article
- Article
- Accurate identification of single-cell types via correntropy-based Sparse PCA combining hypergraph and fusion similarity.Journal of applied statistics · 2025Article
- Maximum Correntropy Linear Prediction for Voice Inverse Filtering: Theoretical Framework and Practical Implementation.IEEE transactions on audio, speech, and language processing (2025) · 2025Article
- Metabolism-associated molecular classification and prognosis signature of head and neck squamous cell carcinoma.Heliyon · 2024Article
- A new gene-scoring method for uncovering novel glaucoma-related genes using non-negative matrix factorization based on RNA-seq data.Frontiers in genetics · 2023Article
- Alternative splicing and alternative polyadenylation define tumor immune microenvironment and pharmacogenomic landscape in clear cell renal carcinoma.Molecular therapy. Nucleic acids · 2022Article
- Artificial intelligence in clinical research of cancers.Briefings in bioinformatics · 2022Article
- NMFNA: A Non-negative Matrix Factorization Network Analysis Method for Identifying Modules and Characteristic Genes of Pancreatic Cancer.Frontiers in genetics · 2021Article
- Analysis of Cyclin-Dependent Kinase 1 as an Independent Prognostic Factor for Gastric Cancer Based on Statistical Methods.Frontiers in cell and developmental biology · 2020Article
- Clustering Sparse Data With Feature Correlation With Application to Discover Subtypes in Cancer.IEEE access : practical innovations, open solutions · 2020Article
- HPOAnnotator: improving large-scale prediction of HPO annotations by low-rank approximation with HPO semantic similarities and multiple PPI networks.BMC medical genomics · 2019Article
- A Sparse Non-Negative Matrix Factorization Framework for Identifying Functional Units of Tongue Behavior From MRI.IEEE transactions on medical imaging · 2019Article
- CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer.BMC bioinformatics · 2019Article
- Scalable non-negative matrix tri-factorization.BioData mining · 2017Article
- Differential Regulatory Analysis Based on Coexpression Network in Cancer Research.BioMed research international · 2016Review
- A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.PloS one · 2014Article
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3 authors.
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Abstract
backgroundNon-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l2 norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise.
resultsWe propose a maximum correntropy criterion (MCC)-based NMF method (NMF-MCC) for gene expression data-based cancer clustering. Instead of minimizing the l2 norm or the Kullback-Leibler distance, NMF-MCC maximizes the correntropy between the product of the two matrices and the original matrix. The optimization problem can be solved by an expectation conditional maximization algorithm.
conclusionsExtensive experiments on six cancer benchmark sets demonstrate that the proposed method is significantly more accurate than the state-of-the-art methods in cancer clustering.
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