ArticleBMC bioinformatics2020
Optimization and expansion of non-negative matrix factorization.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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43 citing papers in PubMed, 118 citations in OpenAlex.
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- Non-negative matrix factorization algorithms generally improve topic model fits.Statistics and computing · 2026Article
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- Comparative evaluation of reference-free transcriptomic deconvolution highlights the importance of biological validation in astrocytes across Alzheimer's disease.Frontiers in bioinformatics · 2026Article
- Interpretable phenotype decoding from multicondition sequencing data with ALPINE.Genome research · 2025Article
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- Covariate-moderated Empirical Bayes Matrix Factorization.Advances in neural information processing systems · 2025Article
- An optimal pairwise merge algorithm improves the quality and consistency of nonnegative matrix factorization.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2025Article
- Identification of HER2-positive breast cancer molecular subtypes with potential clinical implications in the ALTTO clinical trial.Nature communications · 2024Article
- Assessing the measurement invariance of Free Will and Determinism Plus scale across four languages: a registered report.Royal Society open science · 2024Article
- IMMUNE AND MOLECULAR CORRELATES OF RESPONSE TO IMMUNOTHERAPY REVEALED BY BRAIN-METASTATIC MELANOMA MODELS.bioRxiv : the preprint server for biology · 2024Article
- Mapping spatially resolved transcriptomes in human and mouse pulmonary fibrosis.Nature genetics · 2024Article
- Crosstalk among proximal tubular cells, macrophages, and fibroblasts in acute kidney injury: single-cell profiling from the perspective of ferroptosis.Human cell · 2024Article
- Attention and sentiment of Chinese public toward rural landscape based on Sina Weibo.Scientific reports · 2024Article
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNon-negative matrix factorization (NMF) is a technique widely used in various fields, including artificial intelligence (AI), signal processing and bioinformatics. However existing algorithms and R packages cannot be applied to large matrices due to their slow convergence or to matrices with missing entries. Besides, most NMF research focuses only on blind decompositions: decomposition without utilizing prior knowledge. Finally, the lack of well-validated methodology for choosing the rank hyperparameters also raises concern on derived results.
resultsWe adopt the idea of sequential coordinate-wise descent to NMF to increase the convergence rate. We demonstrate that NMF can handle missing values naturally and this property leads to a novel method to determine the rank hyperparameter. Further, we demonstrate some novel applications of NMF and show how to use masking to inject prior knowledge and desirable properties to achieve a more meaningful decomposition.
conclusionsWe show through complexity analysis and experiments that our implementation converges faster than well-known methods. We also show that using NMF for tumour content deconvolution can achieve results similar to existing methods like ISOpure. Our proposed missing value imputation is more accurate than conventional methods like multiple imputation and comparable to missForest while achieving significantly better computational efficiency. Finally, we argue that the suggested rank tuning method based on missing value imputation is theoretically superior to existing methods. All algorithms are implemented in the R package NNLM, which is freely available on CRAN and Github.
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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.