Counter-Propagation Neural Networks for Molecular Sequence Classification: Supervised LVQ and Dynamic Node Allocation


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Abstract

A modified counter-propagation (CP) algorithm with supervised learning vector quantizer (LVQ) and dynamic node allocation has been developed for rapid classification of molecular sequences. The molecular sequences were encoded into neural input vectors using an n-gram hashing method for word extraction and a singular value decomposition (SVD) method for vector compression. The neural networks used were three-layered, forward-only CP networks that performed nearest neighbor classification. Several factors affecting the CP performance were evaluated, including weight initialization, Kohonen layer dimensioning, winner selection and weight update mechanisms. The performance of the modified CP network was compared with the back-propagation (BP) neural network and the k-nearest neighbor method. The major advantages of the CP network are its training and classification speed and its capability to extract statistical properties of the input data. The combined BP and CP networks can classify nucleic acid or protein sequences with a close to 100% accuracy at a rate of about one order of magnitude faster than other currently available methods.

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