RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites.

ABSTRACT: Malonylation, which has recently emerged as an important lysine modification, regulates diverse biological activities and has been implicated in several pervasive disorders, including cardiovascular disease and cancer. However, conventional global proteomics analysis using tandem mass spectrometry can be time-consuming, expensive and technically challenging. Therefore, to complement and extend existing experimental methods for malonylation site identification, we developed two novel computational methods for malonylation site prediction based on random forest and deep learning machine learning algorithms, RF-MaloSite and DL-MaloSite, respectively. DL-MaloSite requires the primary amino acid sequence as an input and RF-MaloSite utilizes a diverse set of biochemical, physiochemical and seque

SUBMITTER: Al-Barakati H 

PROVIDER: S-EPMC7160427 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature