Harnessing Deep Learning for Omics in an Era of COVID-19.
OMICS
; 27(4): 141-152, 2023 04.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2297045
ABSTRACT
Omics data are multidimensional, heterogeneous, and high throughput. Robust computational methods and machine learning (ML)-based models offer new prospects to accelerate the data-to-knowledge trajectory. Deep learning (DL) is a powerful subset of ML inspired by brain structure and has created unprecedented momentum in bioinformatics and computational biology research. This article provides an overview of the current DL models applied to multi-omics data for both the beginner and the expert user. Additionally, COVID-19 will continue to impact planetary health as a pandemic and an endemic disease, with genomic and multi-omic pathophysiology. DL offers, therefore, new ways of harnessing systems biology research on COVID-19 diagnostics and therapeutics. Herein, we discuss, first, the statistical ML algorithms and essential deep architectures. Then, we review DL applications in multi-omics data analysis and their intersection with COVID-19. Finally, challenges and several promising directions are highlighted going forward in the current era of COVID-19.
Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
/
COVID-19
Límite:
Humanos
Idioma:
Inglés
Revista:
OMICS
Asunto de la revista:
Biologia Molecular
Año:
2023
Tipo del documento:
Artículo
País de afiliación:
Omi.2022.0155
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