Bioinformatics Data Mining Using Artificial Immune Systems and Neural Networks

Shane Dixon, California Polytechnic State University - San Luis Obispo
Xiao-Hua Yu, California Polytechnic State University - San Luis Obispo

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Abstract

Bioinformatics is a data-intensive field of research and development. The purpose of bioinformatics data mining is to discover the relationships and patterns in large databases to provide useful information for biomedical analysis and diagnosis. In this research, algorithms based on artificial immune systems (AIS) and artificial neural networks (ANN) are employed for bioinformatics data mining. Three different variations of the real-valued negative selection algorithm and a multi-layer feedforward neural network model are discussed, tested and compared via computer simulations. It is shown that the ANN model yields the best overall result while the AIS algorithm is advantageous when only the “normal” (or “self”) data is available.