Machine learning for discovering missing or wrong protein function annotations: A comparison using updated benchmark datasets

BACKGROUND: A massive amount of proteomic data is generated on a daily basis, nonetheless annotating all sequences is costly and often unfeasible. As a countermeasure, machine learning methods have been used to automatically annotate new protein functions. More specifically, many studies have invest...

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Detalles Bibliográficos
Autores principales: Nakano, Felipe Kenji, Lietaert, Mathias, Vens, Celine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755698/
https://www.ncbi.nlm.nih.gov/pubmed/31547800
http://dx.doi.org/10.1186/s12859-019-3060-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755698/
https://www.ncbi.nlm.nih.gov/pubmed/31547800
http://dx.doi.org/10.1186/s12859-019-3060-6

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