Identifying Lysine Succinylation Sites in Proteins by Broad Learning System and Optimizing Imbalanced Training Dataset via Randomly Labeling Samples

摘要

As one important type of post-translational modifications(PTMs),protein lysine succinylation regulates many important biological processes.It is also closely involved with some major diseases in the aspects of Cardiometabolic,liver metabolic,nervous system and so on.Therefore,it is imperative to predict the succinylation sites in proteins for both basic research and drug development.In this paper,a novel predictor called i Succ Lys-BLS was proposed by not only introducing a new machine learning algorithm—Broad Learning System,but also optimizing the imbalanced data by randomly labeling samples.Rigorous cross-validation and independent test indicate that the success rate of i Succ Lys-BLS for positive samples is overwhelmingly higher than its counterparts.