{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["6(1)"],"submitter":["Mencattini A"],"pubmed_abstract":["One of the major problems in bioimaging, often highly underestimated, is whether features extracted for a discrimination or regression task will remain valid for a broader set of similar experiments or in the presence of unpredictable perturbations during the image acquisition process. Such an issue is even more important when it is addressed in the context of deep learning features due to the lack of a priori known relationship between the black-box descriptors (deep features) and the phenotypic properties of the biological entities under study. In this regard, the widespread use of descriptors, such as those coming from pre-trained Convolutional Neural Networks (CNNs), is hindered by the fact that they are devoid of apparent physical meaning and strongly subjected to unspecific biases, i"],"journal":["Communications biology"],"pagination":["241"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9984362"],"repository":["biostudies-literature"],"pubmed_title":["Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis."],"pmcid":["PMC9984362"],"pubmed_authors":["Di Giuseppe D","Comes MC","Antonelli G","Corsi F","Casti P","Filippi J","Ghibelli L","Mencattini A","Martinelli E","D'Orazio M","Di Natale C","Veith I","Parrini MC"],"additional_accession":[]},"is_claimable":false,"name":"Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis.","description":"One of the major problems in bioimaging, often highly underestimated, is whether features extracted for a discrimination or regression task will remain valid for a broader set of similar experiments or in the presence of unpredictable perturbations during the image acquisition process. Such an issue is even more important when it is addressed in the context of deep learning features due to the lack of a priori known relationship between the black-box descriptors (deep features) and the phenotypic properties of the biological entities under study. In this regard, the widespread use of descriptors, such as those coming from pre-trained Convolutional Neural Networks (CNNs), is hindered by the fact that they are devoid of apparent physical meaning and strongly subjected to unspecific biases, i","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Mar","modification":"2026-07-15T01:38:14.007Z","creation":"2025-04-04T11:35:38.104Z"},"accession":"S-EPMC9984362","cross_references":{"pubmed":["36869080"],"doi":["10.1038/s42003-023-04585-9"]}}