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PeptideMind: applying machine learning algorithms to assess replicate quality in shotgun proteomic data
[article]
2020
bioRxiv
pre-print
Assessment of replicate quality is an important process for any shotgun proteomics experiment. One fundamental question in proteomics data analysis is whether any specific replicates in a set of analyses are biasing the downstream comparative quantitation. In this paper, we present an experimental method to address such a concern. PeptideMind uses a series of clustering Machine Learning algorithms to assess outliers when comparing proteomics data from two states with six replicates each. The
doi:10.1101/2020.08.20.260455
fatcat:qgwaksatpjebdncbwged3tzpmq