Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization

High-throughput biological technologies (e.g. ChIP-seq, RNA-seq and single-cell RNA-seq) rapidly accelerate the accumulation of genome-wide omics data in diverse interrelated biological scenarios (e.g. cells, tissues and conditions). Integration and differential analysis are two common paradigms for...

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Detalles Bibliográficos
Autores principales: Zhang, Lihua, Zhang, Shihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Computational Biology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6649783/
https://www.ncbi.nlm.nih.gov/pubmed/31175825
http://dx.doi.org/10.1093/nar/gkz488

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6649783/
https://www.ncbi.nlm.nih.gov/pubmed/31175825
http://dx.doi.org/10.1093/nar/gkz488

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