title: Efficient recognition of immunoglobulin domains from amino acid sequences using a neural network.
abstract: A neural network was trained using back propagation to recognize immunoglobulin domains from amino acid sequences. The program was designed to identify proteins exhibiting such domains with minimal rates of false positives and false negatives. The National Biomedical Research Foundation NEW protein sequences database was scanned to evaluate the performance of the program in recognizing mouse immunoglobulin sequences. The program correctly recognized 55 out of 56 mouse immunoglobulin sequences, corresponding to a recognition efficiency of 98.2% with an overall false positive rate of 7.3%. These data demonstrate that neural network-based search programs are well suited to search for sequences characterized by only a few well-conserved subsequences.
authors: School of Computer Science, McGill University, Montréal, Québec, Canada.
authors: Y
authors: Y
authors: Bengio
authors: Y
authors: Y
authors: Pouliot
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D001185:Artificial Intelligence; D002417:Cattle; D005188:False Negative Reactions; D006801:Humans; D007136:Immunoglobulins / Q000737:chemistry*; D051379:Mice; D008838:Microcomputers; D008969:Molecular Sequence Data; D051381:Rats; D012984:Software
chemical_list: D007136:Immunoglobulins