A cost-sensitive online learning method for peptide identification.

ABSTRACT:

Background

Post-database search is a key procedure in peptide identification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of peptide identification, the challenge remains on large-scale datasets and datasets with a distribution of unbalanced PSMs. A more efficient learning strategy is required for improving the accuracy of peptide identification on challenging datasets. While complex learning models have larger power of classification, they may cause overfitting problems and introduce computational complexity on large-scale datasets. Kernel methods map data from the sample space to high dimens

SUBMITTER: Liang X 

PROVIDER: S-EPMC7183122 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature