Computational Method for Identifying Malonylation Sites by Using Random Forest Algorithm

摘要

Background: As a newly uncovered post-translational modification on the c-amino group of lysine residue, protein malonylation was found to be involved in metabolic pathways and certain diseases. Apart from experimental approaches, several computational methods based on machine learning algorithms were recently proposed to predict malonylation sites. However, previous methods failed to address imbalanced data sizes between positive and negative samples.