Wide and deep learning for automatic cell type identification.
Classification
Deep learning
Single cell data
Journal
Computational and structural biotechnology journal
ISSN: 2001-0370
Titre abrégé: Comput Struct Biotechnol J
Pays: Netherlands
ID NLM: 101585369
Informations de publication
Date de publication:
2021
2021
Historique:
received:
30
09
2020
revised:
16
01
2021
accepted:
18
01
2021
entrez:
22
2
2021
pubmed:
23
2
2021
medline:
23
2
2021
Statut:
epublish
Résumé
Cell type classification is an important problem in cancer research, especially with the advent of single cell technologies. Correctly identifying cells within the tumor microenvironment can provide oncologists with a snapshot of how a patient's immune system reacts to the tumor. Wide and deep learning (WDL) is an approach to construct a cell-classification prediction model that can learn patterns within high-dimensional data (deep) and ensure that biologically relevant features (wide) remain in the final model. In this paper, we demonstrate that regularization can prevent overfitting and adding a wide component to a neural network can result in a model with better predictive performance. In particular, we observed that a combination of dropout and
Identifiants
pubmed: 33613870
doi: 10.1016/j.csbj.2021.01.027
pii: S2001-0370(21)00031-3
pmc: PMC7878986
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1052-1062Subventions
Organisme : NCI NIH HHS
ID : R01 CA124515
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA157664
Pays : United States
Informations de copyright
© 2021 The Authors.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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