SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data.

ABSTRACT: A major challenge in mass spectrometry-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. Machine learning techniques are promising approaches for leveraging large-scale phosphoproteomics data to computationally predict substrates of kinases. However, the small number of experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together limit their applicability and utility. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, and both traditional and deep learning models, we first demonstrate that a

SUBMITTER: Xiao D 

PROVIDER: S-EPMC10632189 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature