iSUMO-RsFPN: A predictor for identifying lysine SUMO-ylation sites based on multi-features and feature pyramid networks

In this study, we constructed a novel deep neural network Residual Pyramid Network (RsFPN), and developed an ensemble deep learning predictor called iSUMO-RsFPN. Initially, three feature extraction methods were employed to extract features from samples. Following this, weak classifiers were trained based on RsFPN for each feature type. Ultimately, the weak classifiers were integrated to construct the final classifier. Moreover, the predictor underwent systematically testing on an independent test dataset, where the results demonstrated a significant improvement over the existing state-of-the-art predictors. The code of iSUMO-RsFPN is free and available at https://github.com/454170054/iSUMO-RsFPN.PMID:38191118 | DOI:10.1016/j.ab.2024.115460
Source: Analytical Biochemistry - Category: Biochemistry Authors: Source Type: research