Identifying Ovarian Cancer-Associated EV mRNA Expression Profiles Using Unsupervised Machine Learning and Non-Negative Matrix Factorization

Extracellular vesicle (EV) transcriptomic data provides a high-dimensional representation of cellular states but remains challenging to interpret due to noise, redundancy, and limited sample sizes. Most existing approaches rely on supervised differential expression analyses, which can be biased and...

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Detalhes bibliográficos

Principais autores:	Rama Krishna Thelagathoti, Chao Jiang, Dinesh S. Chandel, Wesley A. Tom, Cleo Sarmiento, Appolinaire Olou, Gary Krzyzanowski, M. Rohan Fernando
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2026-05-01
coleção:	Bioengineering
Assuntos:	extracellular vesicles non-negative matrix factorization ovarian cancer EV mRNA transcriptomics unsupervised machine learning unsupervised feature selection
Acesso em linha:	https://www.mdpi.com/2306-5354/13/6/597
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