Artículo
Celldeath: A tool for detection of cell death in transmitted light microscopy images by deep learning-based visual recognition
la Greca, Alejandro Damián
; Pérez, Nelba; Castañeda, Sheila Lucia
; Milone, Paula Melania
; Scarafia, Maria Agustina
; Möbbs, Alan Miqueas
; Waisman, Ariel
; Moro, Lucía Natalia
; Sevlever, Gustavo Emilio; Luzzani, Carlos Daniel
; Miriuka, Santiago Gabriel
Fecha de publicación:
24/06/2021
Editorial:
Public Library of Science
Revista:
Plos One
e-ISSN:
1932-6203
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Cell death experiments are routinely done in many labs around the world, these experiments are the backbone of many assays for drug development. Cell death detection is usually performed in many ways, and requires time and reagents. However, cell death is preceded by slight morphological changes in cell shape and texture. In this paper, we trained a neural network to classify cells undergoing cell death. We found that the network was able to highly predict cell death after one hour of exposure to camptothecin. Moreover, this prediction largely outperforms human ability. Finally, we provide a simple python tool that can broadly be used to detect cell death.
Palabras clave:
BIOINFORMATICS
,
ARTIFICIAL INTELLIGENCE
,
APOPTOSIS
,
PLURIPOTENCY
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Articulos(SEDE CENTRAL)
Articulos de SEDE CENTRAL
Articulos de SEDE CENTRAL
Citación
la Greca, Alejandro Damián; Pérez, Nelba; Castañeda, Sheila Lucia; Milone, Paula Melania; Scarafia, Maria Agustina; et al.; Celldeath: A tool for detection of cell death in transmitted light microscopy images by deep learning-based visual recognition; Public Library of Science; Plos One; 16; 6; 24-6-2021; 1-15
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