Machine learning analysis of microbial flow cytometry data from nanoparticles, antibiotics and carbon sources perturbed anaerobic microbiomes

BACKGROUND: Flow cytometry, with its high throughput nature, combined with the ability to measure an increasing number of cell parameters at once can surpass the throughput of prevalent genomic and metagenomic approaches in the study of microbiomes. Novel computational approaches to analyze flow cyt...

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Detalles Bibliográficos
Autores principales: Dhoble, Abhishek S., Lahiri, Pratik, Bhalerao, Kaustubh D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Methodology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134764/
https://www.ncbi.nlm.nih.gov/pubmed/30220912
http://dx.doi.org/10.1186/s13036-018-0112-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134764/
https://www.ncbi.nlm.nih.gov/pubmed/30220912
http://dx.doi.org/10.1186/s13036-018-0112-9

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