Gemini: memory-efficient integration of hundreds of gene networks with high-order pooling

MOTIVATION: The exponential growth of genomic sequencing data has created ever-expanding repositories of gene networks. Unsupervised network integration methods are critical to learn informative representations for each gene, which are later used as features for downstream applications. However, the...

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Detalles Bibliográficos
Autores principales: Woicik, Addie, Zhang, Mingxin, Xu, Hanwen, Mostafavi, Sara, Wang, Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Systems Biology and Networks
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311345/
https://www.ncbi.nlm.nih.gov/pubmed/37387142
http://dx.doi.org/10.1093/bioinformatics/btad247

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311345/
https://www.ncbi.nlm.nih.gov/pubmed/37387142
http://dx.doi.org/10.1093/bioinformatics/btad247

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