Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

Background: All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences. Results: The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Maria Pamela C. David, Gisela P. Concepcion, Eduardo A. Padlan
• Keywords: Amino Acid Composition, Bayesian Classifier, BMCBI 2010, Naive Bayesian Classifier