Efficient recognition of immunoglobulin domains from amino acid sequences using a neural network
- 31 December 1989
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 6 (4) , 319-324
- https://doi.org/10.1093/bioinformatics/6.4.319
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 rares 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 as well suited to search for sequences characterized by only a few well-conserved subsequences.This publication has 5 references indexed in Scilit:
- Protein secondary structure prediction with a neural network.Proceedings of the National Academy of Sciences, 1989
- Predicting the secondary structure of globular proteins using neural network modelsJournal of Molecular Biology, 1988
- Learning Internal Representations by Error PropagationPublished by Elsevier ,1988
- Profile analysis: detection of distantly related proteins.Proceedings of the National Academy of Sciences, 1987
- Rapid similarity searches of nucleic acid and protein data banks.Proceedings of the National Academy of Sciences, 1983