Bioinformatics Data Mining Using Artificial Immune Systems and Neural Networks

Author Info

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

Recommended Citation

Published in Proceedings of the 2010 IEEE International Conference on Information and Automation, June 20, 2010, pages 440-445.

The definitive version is available at https://doi.org/10.1109/ICINFA.2010.5512376.

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.

Disciplines

Electrical and Computer Engineering

Copyright

2010 IEEE.

Publisher statement

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