Boosting Granular Support Vector Machines for the Accurate Prediction of Protein-Nucleotide Binding Sites.
Journal:
Combinatorial chemistry & high throughput screening
Published Date:
Jan 1, 2019
Abstract
AIM AND OBJECTIVE: The accurate identification of protein-ligand binding sites helps elucidate protein function and facilitate the design of new drugs. Machine-learning-based methods have been widely used for the prediction of protein-ligand binding sites. Nevertheless, the severe class imbalance phenomenon, where the number of nonbinding (majority) residues is far greater than that of binding (minority) residues, has a negative impact on the performance of such machine-learning-based predictors.