Computational intelligence - a broad initiative in automated learning from sequences

Abstract

In our attempts to construct methods for automated structural prediction and annotation of proteins as well as automated drug design and discovery, the identification of structure and function from the primary structure of a protein is an important, but difficult problem. We extract features using biophysical properties of the different amino acids and using the patterns of poly-peptide sequences. Based on these features we construct different predictors for different tasks. We demonstrate that our classifiers compare favorably to existing classifiers, and we experiment with the use of ensemble methods to enhance our predictors' accuracies and explaining powers. We showed the synergy of approaches from computational intelligence and biophysics is powerful. This work has particular relevance for the study of ion-channels, ligand binding sites, and alternative splicing