Learning from genome sequences utilizing computational intelligence

Abstract

Advances in genome sequencing technology have led to an exploration in the amount of sequence data available, learning from proteins coded for by genomes is a difficult task. Bioinformatics is thus a burgeoning field that holds great promise for deepening our understanding of biochemical pathways, for understanding the genetic differences between species and how they arose, and for understanding the genetic basis of various disease processes. We developed a method for classification and knowledge discovery in membrane and intrinsic unstructured/disordered proteins (IUP). We analyzed the amino acid compositions and biophysical properties of proteins. Our joint transmembrane and IUP predictor utilized biophysical characterizations, feature generation, feature selection and computational intelligence as well as ensemble methods to improve the accuracies and performances