Residue-Residue Interaction Prediction via Stacked Meta-Learning.

ABSTRACT: Protein-protein interactions (PPIs) are the basis of most biological functions determined by residue-residue interactions (RRIs). Predicting residue pairs responsible for the interaction is crucial for understanding the cause of a disease and drug design. Computational approaches that considered inexpensive and faster solutions for RRI prediction have been widely used to predict protein interfaces for further analysis. This study presents RRI-Meta, an ensemble meta-learning-based method for RRI prediction. Its hierarchical learning structure comprises four base classifiers and one meta-classifier to integrate predictive strengths from different classifiers. It considers multiple feature types, including sequence-, structure-, and neighbor-based features, for characterizing other properties

SUBMITTER: Chen KH 

PROVIDER: S-EPMC8232778 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature