Sequence Feature Analysis and Classification

Wu and Shivakumar (1994) developed a neural network system for classification of ribosomal RNAs according to phylogenetic classes. Two separate networks were trained, one for 220 small subunit classes, and the other for 15 large subunit classes. The input sequences were encoded using various n-gram encoding schemes, followed by a singular value decomposition compression to different number of reduced dimensions. It was 

Sun et al. (1995) applied a three-layer back-propagation network to classify transfer RNA gene sequences according to their source organisms. The evolutionary relationship derived from this study was consistent with those from other methods. 

Abremski, K., Sirotkin, K. Lapedes, A (1991). Application of neural networks and information theory to the identification of E.coli transcriptional promoters. Math Model Sci Comput 2, 634-41. 

Arrigo, P., Giuliano, F., Scalia, F., Rapallo, A. Damiani, G. (1991). Identification of a new motif on nucleic acid sequence data using Kdhonen s self organizing map. Comput Appl Biosci 7, 353-7. 

Bisant, D. Maizd, J. (1995). Identification of ribosome binding sites in Escherichia coli using neural network models. Nucleic Acids Res 23,1632-9. 

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