Artificial intelligence systems based on texture descriptors for vaccine development
peptide classification, vaccine development, HIV-1 protease prediction, locally binary patterns, discrete cosine transform, support vector machine
The aim of this work is to analyze and compare several feature extraction methods for peptide classification that are based on the calculation of texture descriptors starting from a matrix representation of the peptide. This texture-based representation of the peptide is then used to train a support vector machine classifier. In our experiments, the best results are obtained using local binary patterns variants and the discrete cosine transform with selected coefficients. These results are better than those previously reported that employed texture descriptors for peptide representation. In addition, we perform experiments that combine standard approaches based on amino acid sequence. The experimental section reports several tests performed on a vaccine dataset for the prediction of peptides that bind human leukocyte antigens and on a human immunodeficiency virus (HIV-1). Experimental results confirm the usefulness of our novel descriptors.
Nanni, Loris, Sheryl Brahnam, and Alessandra Lumini. "Artificial intelligence systems based on texture descriptors for vaccine development." Amino acids 40, no. 2 (2011): 443-451.
DOI for the article
Management and Information Technology