BAMPDA: a Matrix Refactoring Framework with Heterogeneous Inference for Potential PTM-disease Association Identification.
TL;DR
BAMPDA is developed, a matrix refactoring framework with heterogeneous inference designed to enhance the inference of PTM-disease associations and rigorous 5-fold cross-validation demonstrates that BAM PDA significantly outperforms five recent baseline methods.
Abstract
Post-translational modifications (PTMs) are modifications of proteins that occur after translation. They exert their influence on human health by altering the properties of proteins. Recent progress in biomedical research has yielded extensive heterogeneous datasets detailing protein PTMs and their disease relevance. These datasets provide a substantial foundation for the development of methods predicting PTM-disease associations. Thus we develop BAMPDA, a matrix refactoring framework with heterogeneous inference designed to enhance the inference of PTM-disease associations. Our approach integrates multi source data encompassing both protein and disease characteristics, leveraging two successively applied matrix based algorithms for association prediction. Furthermore, rigorous 5-fold cross-validation demonstrates that BAM PDA significantly outperforms five recent baseline methods. Finally, we applied BAMPDA to four representative diseases to predict associated proteins and validate the role of PTMs in mediating these disease relationships. The results underscore BAMPDA's robust predictive capability in practical scenarios.