Results 11 to 20 of about 142,276 (359)

Scalable non-negative matrix tri-factorization

BioData Mining, 2017
Background Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining.
Andrej Čopar, Marinka žitnik, Blaž Zupan   +2 more
doaj   +3 more sources

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine. [PDF]

Brief Bioinform, 2022
The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field.
Hamamoto R, Takasawa K, Machino H, Kobayashi K, Takahashi S, Bolatkan A, Shinkai N, Sakai A, Aoyama R, Yamada M, Asada K, Komatsu M, Okamoto K, Kameoka H, Kaneko S.   +14 more
europepmc   +2 more sources

Stretched non-negative matrix factorization

npj Computational Materials
A novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis.
Ran Gu, Yevgeny Rakita, Ling Lan, Zach Thatcher, Gabrielle E. Kamm, Daniel O’Nolan, Brennan Mcbride, Allison Wustrow, James R. Neilson, Karena W. Chapman, Qiang Du, Simon J. L. Billinge   +11 more
doaj   +3 more sources

Non-negative Matrix Factorization [PDF]

Definitions, 2020
Linear dimensionality reduction techniques such as principal component analysis and singular value decomposition are powerful tools for dealing with high dimensional data. In this report, we will explore a linear dimensionality reduction technique namely
Morten Mørup, Kristoffer Hougaard Madsen, Lars Kai Hansen, Mikkel N. Schmidt   +3 more
semanticscholar   +2 more sources

Graph regularized non-negative matrix factorization with $$L_{2,1}$$ L 2 , 1 norm regularization terms for drug–target interactions prediction [PDF]

BMC Bioinformatics, 2023
Background Identifying drug–target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are costly and time consuming. Effective computational methods to predict DTIs are useful to speed up the process of
Junjun Zhang, Minzhu Xie
doaj   +2 more sources

Investigating microstructural variation in the human hippocampus using non-negative matrix factorization

NeuroImage, 2020
In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to ...
Raihaan Patel, Christopher J. Steele, Anthony G.X. Chen, Sejal Patel, Gabriel A. Devenyi, Jürgen Germann, Christine L. Tardif, M. Mallar Chakravarty   +7 more
doaj   +2 more sources

Topic supervised non-negative matrix factorization [PDF]

, 2017
Topic models have been extensively used to organize and interpret the contents of large, unstructured corpora of text documents. Although topic models often perform well on traditional training vs.
MacMillan, Kelsey, Wilson, James D.
core   +4 more sources

Bayesian multi-study non-negative matrix factorization for mutational signatures [PDF]

Genome Biology
Mutational signatures are typically identified from tumor genome sequencing data using non-negative matrix factorization (NMF). However, existing NMF techniques only decompose a single dataset, limiting rigorous comparisons of signatures across ...
Isabella N. Grabski, Lorenzo Trippa, Giovanni Parmigiani   +2 more
doaj   +2 more sources

Blind source separation with optimal transport non-negative matrix factorization

EURASIP Journal on Advances in Signal Processing, 2018
Optimal transport as a loss for machine learning optimization problems has recently gained a lot of attention. Building upon recent advances in computational optimal transport, we develop an optimal transport non-negative matrix factorization (NMF ...
Antoine Rolet, Vivien Seguy, Mathieu Blondel, Hiroshi Sawada   +3 more
doaj   +3 more sources

Autoencoder-like Sparse Non-Negative Matrix Factorization with Structure Relationship Preservation [PDF]

Entropy
Clustering algorithms based on non-negative matrix factorization (NMF) have garnered significant attention in data mining due to their strong interpretability and computational simplicity.
Ling Zhong, Haiyan Gao
doaj   +2 more sources