Hierarchical deep learning for predicting GO annotations by integrating protein knowledge

MOTIVATION: Experimental testing and manual curation are the most precise ways for assigning Gene Ontology (GO) terms describing protein functions. However, they are expensive, time-consuming and cannot cope with the exponential growth of data generated by high-throughput sequencing methods. Hence,...

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Detalles Bibliográficos
Autores principales: Merino, Gabriela A, Saidi, Rabie, Milone, Diego H, Stegmayer, Georgina, Martin, Maria J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Original Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524999/
https://www.ncbi.nlm.nih.gov/pubmed/35929781
http://dx.doi.org/10.1093/bioinformatics/btac536

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524999/
https://www.ncbi.nlm.nih.gov/pubmed/35929781
http://dx.doi.org/10.1093/bioinformatics/btac536

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