Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response

High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from genes to cells, from cells to organs, and through the...

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Detalles Bibliográficos
Autores principales: D’Orazio, M., Murdocca, M., Mencattini, A., Casti, P., Filippi, J., Antonelli, G., Di Giuseppe, D., Comes, M. C., Di Natale, C., Sangiuolo, F., Martinelli, E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9123013/
https://www.ncbi.nlm.nih.gov/pubmed/35595808
http://dx.doi.org/10.1038/s41598-022-12364-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9123013/
https://www.ncbi.nlm.nih.gov/pubmed/35595808
http://dx.doi.org/10.1038/s41598-022-12364-5

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