Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis

A. Mencattini; M. D’Orazio; P. Casti; M. C. Comes; D. Di Giuseppe; G. Antonelli; J. Filippi; F. Corsi; L. Ghibelli; I. Veith; C. Di Natale; M. C. Parrini; E. Martinelli

doi:10.1038/s42003-023-04585-9

Communications Biology (Mar 2023)

Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis

A. Mencattini,
M. D’Orazio,
P. Casti,
M. C. Comes,
D. Di Giuseppe,
G. Antonelli,
J. Filippi,
F. Corsi,
L. Ghibelli,
I. Veith,
C. Di Natale,
M. C. Parrini,
E. Martinelli

Affiliations

A. Mencattini: Department of Electronic Engineering, University of Rome Tor Vergata
M. D’Orazio: Department of Electronic Engineering, University of Rome Tor Vergata
P. Casti: Department of Electronic Engineering, University of Rome Tor Vergata
M. C. Comes: Department of Electronic Engineering, University of Rome Tor Vergata
D. Di Giuseppe: Department of Electronic Engineering, University of Rome Tor Vergata
G. Antonelli: Department of Electronic Engineering, University of Rome Tor Vergata
J. Filippi: Department of Electronic Engineering, University of Rome Tor Vergata
F. Corsi: Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (IC-LOC), University of Rome Tor Vergata
L. Ghibelli: Department of Biology, University of Rome Tor Vergata
I. Veith: Inserm U830, Stress and Cancer Lab, Institut Curie, Centre de Recherche, Paris Sciences et Lettres Research University
C. Di Natale: Department of Electronic Engineering, University of Rome Tor Vergata
M. C. Parrini: Inserm U830, Stress and Cancer Lab, Institut Curie, Centre de Recherche, Paris Sciences et Lettres Research University
E. Martinelli: Department of Electronic Engineering, University of Rome Tor Vergata

DOI: https://doi.org/10.1038/s42003-023-04585-9
Journal volume & issue: Vol. 6, no. 1
pp. 1 – 17

Abstract

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Deep-Manager is a software platform that enables efficient selection of features with lower sensitivity to unspecified disturbances across sets of similar experiments in bioimaging.

Published in Communications Biology

ISSN: 2399-3642 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General)
Website: https://www.nature.com/commsbio/

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