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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2019
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| 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 |