In this project, a neural network based system is used to classify the promoter regions found in Escherichia coli DNA sequences. An unsupervised algorithm based on the self-organizing feature map is used to classify the sequences and a dynamic programming algorithm is used too query the trained neural networks. In order to generalize the neural network's weights for display purposes, a back propagation supervised learning algorithm based on the conjugate gradient method is used to map the weights to one of the fifteen combinations of adenine, cytosine, guanine, and thymine (the chemical components of DNA). The results show that this method is able to classify the training sequences into discrete sub-classes which provide a query base for classifying new sequences. This method can be used for any class of sequences and can be extended for use in searching sequence databases. / Thesis / Master of Science (MS)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/23259 |
| Date | 04 1900 |
| Creators | Levy, Michael |
| Contributors | Harley, Calvin, Computation |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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