Machine Learning Strategy That Leverages Large Data sets to Boost Statistical Power in Small-Scale Experiments.

ABSTRACT: 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 semisupervised algorithms to learn models directly from the data sets that they analyze. Although these methods are effective for many proteomics experiments, we suspected that they may be suboptimal for experiments of smaller scale. In this work, we found that the power and consistency of Percolator results were reduced as the size of the experiment was decreased. As an alternative, we propose a different operating mode for Percolator: learn a model with Percolator from a large data set and use the learned model to evaluate the small-scale experiment. We call this a "static modeling" approach, in contra

SUBMITTER: Fondrie WE 

PROVIDER: S-EPMC8455073 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature