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M. Tompa, N. Li, T. L. Bailey, G. M. Church, B. De Moor, E. Eskin, A. V. Favorov, M. C. Frith, Y. Fu, W. J. Kent, V. J. Makeev, A. A. Mironov, W. S. Noble, G. Pavesi, G. Pesole, M. Regnier, N. Simonis, S. Sinha, G. Thijs, J. van Helden, M. Vandenbogaert, Z. Weng, C. Workman, C. Ye and Z. Zhu, “Assessing Computational Tools for the Discovery of Transcription Factor Binding Sites,” Nature Biotechnology, Vol. 23, No. 1, 2005, pp. 137-144. doi:10.1038/nbt1053

has been cited by the following article:

• TITLE: Enhanced Self-Organizing Map Neural Network for DNA Sequence Classification

  AUTHORS: Marghny Mohamed, Abeer A. Al-Mehdhar, Mohamed Bamatraf, Moheb R. Girgis

  KEYWORDS: Bioinformatics; Artificial Neural Networks; Self-Organizing Map; Classification; Sequence Alignment

  JOURNAL NAME: Intelligent Information Management, Vol.5 No.1, January 24, 2013

  ABSTRACT: The artificial neural networks (ANNs), among different soft computing methodologies are widely used to meet the challenges thrown by the main objectives of data mining classification techniques, due to their robust, powerful, distributed, fault tolerant computing and capability to learn in a data-rich environment. ANNs has been used in several fields, showing high performance as classifiers. The problem of dealing with non numerical data is one major obstacle prevents using them with various data sets and several domains. Another problem is their complex structure and how hands to interprets. Self-Organizing Map (SOM) is type of neural systems that can be easily interpreted, but still can’t be used with non numerical data directly. This paper presents an enhanced SOM structure to cope with non numerical data. It used DNA sequences as the training dataset. Results show very good performance compared to other classifiers. For better evaluation both micro-array structure and their sequential representation as proteins were targeted as dataset accuracy is measured accordingly.