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Título del libro: Bioinformatics And Medical Applications: Big Data Using Deep Learning Algorithms
Título del capítulo: Computational predictors of the predominant protein function: SARS-CoV-2 case

Autores UNAM:
CARLOS POLANCO GONZALEZ; MANLIO FABIO MARQUEZ MURILLO; GILBERTO VARGAS ALARCON;
Autores externos:

Idioma:

Año de publicación:
2022
Palabras clave:

Adenoviridae; Advantages; Algorithms; Anelloviridae; Arenaviridae; Caliciviridae; Computational predictions; Coronaviridae family; Disadvantages; DNA virus; Herpesviridae; Herpesviridae; Linear representation; Non-supervised algorithms; Papillomaviridae; Parvoviridae; Picornaviridae; PIM® profile; Polarity Index Method®; Poxviridae; Putative proteins; Reoviridae; Rhabdoviridae; RNA virus; SARS-CoV-2; Supervised algorithms


Resumen:

In this chapter, we describe the main molecular features of SARS-CoV-2 that cause COVID-19 disease, as well as a high-efficiency computational prediction called Polarity Index Method®. We also introduce a molecular classification of the RNA virus and DNA virus families and two main classifications: supervised and non-supervised algorithms of the predictions of the predominant function of proteins. Finally, some results obtained by the proposed non-supervised method are given, as well as some particularities found about the linear representation of proteins. © 2022 Scrivener Publishing LLC.


Entidades citadas de la UNAM: