title: Identifying DNA splice sites using hypernetworks with artificial molecular evolution.
abstract: Identifying DNA splice sites is a main task of gene hunting. We introduce the hyper-network architecture as a novel method for finding DNA splice sites. The hypernetwork architecture is a biologically inspired information processing system composed of networks of molecules forming cells, and a number of cells forming a tissue or organism. Its learning is based on molecular evolution. DNA examples taken from GenBank were translated into binary strings and fed into a hypernetwork for training. We performed experiments to explore the generalization performance of hypernetwork learning in this data set by two-fold cross validation. The hypernetwork generalization performance was comparable to well known classification algorithms. With the best hypernetwork obtained, including local information and heuristic rules, we built a system (HyperExon) to obtain splice site candidates. The HyperExon system outperformed leading splice recognition systems in the list of sequences tested.
authors.affiliation: Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Jose L
authors.initials: JL
authors.lastname: Segovia-Juarez
authors.forename: Silvano
authors.initials: S
authors.lastname: Colombano
authors.forename: Denise
authors.initials: D
authors.lastname: Kirschner
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004247:DNA / Q000235:genetics*; D019143:Evolution, Molecular; D008957:Models, Genetic; D022821:RNA Splice Sites; D049490:Systems Biology
chemical_list: D022821:RNA Splice Sites; D004247:DNA