Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data.

ABSTRACT:

Motivation

Protein phosphorylation is a post-translational modification that underlines various aspects of cellular signaling. A key step to reconstructing signaling networks involves identification of the set of all kinases and their substrates. Experimental characterization of kinase substrates is both expensive and time-consuming. To expedite the discovery of novel substrates, computational approaches based on kinase recognition sequence (motifs) from known substrates, protein structure, interaction and co-localization have been proposed. However, rarely do these methods take into account the dynamic responses of signaling cascades measured from in vivo cellular systems. Given that recent advances in mass spectrometry-based technologies make it possible to quantify phosphorylati

SUBMITTER: Yang P 

PROVIDER: S-EPMC4739180 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature