Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Lambros, Skarlas; | Panos, Ioannidis | Spiridon, Likothanassis
Affiliations: Department of Computer Engineering and Informatics, University of Patras, GR-26500, Rio, Patras, Greece | Abramson Family Cancer Research Institute, 412 Curie Blvd. Philadelphia, PA 19104-6160, University of Pennsylvania, School of Medicine, Philadelphia, USA | Department of Environmental and Natural Resources Management, University of Ioannina, Greece
Note: [] Corresponding author. Tel.: +30 6972676101; E-mail: skarlas@mail.med.upenn.edu
Abstract: Ab initio coding potential prediction in a bacterial genome is an important step in determining an organism's transcriptional regulatory function. Extensive studies of genes structure have been carried out in a few species such as. Escherichia coli, fewer resources exist in newly sequenced genomes like Wolbachia. A model of gene prediction trained on one species may not reflect the properties of other, distantly related prokaryotic organisms. These issues were encountered in the course of predicting genes in the genome of Wolbachia, very important gramnegative bacteria that form intracellular inherited infections in many invertebrates. We describe a coding potential predictor based on artificial neural networks and we compare its performance by using different architectures, learning algorithms and parameters. We rely on a dataset of positive samples constructed from coding sequences and on a negative dataset consisted of all the intergenic regions that were not located between the genes of an operon. Both datasets, positive and negative, were output as fasta formatted files and were used for neural network training. The fast, adaptive, batch learning algorithm Resilient propagation, exhibits the best overall performance on a 64input-10hidden-1output nodes multi-layer perceptron neural network.
Keywords: Gene prediction, prokaryotes, Wolbachia, artificial neural networks, resilient backpropagation
Journal: In Silico Biology, vol. 7, no. 1, pp. 105-113, 2007
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl