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A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments
Machine learning methods have proven invaluable for increasing the sensitivity of peptide detection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, use semi-supervised algorithms to learn models directly from the datasets that they analyze. Although these methods...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455073/ https://www.ncbi.nlm.nih.gov/pubmed/32009418 http://dx.doi.org/10.1021/acs.jproteome.9b00780 |