Bioinformatics data mining using artificial immune systems and neural networks

• Published in: 2010 International Conference on Information and Automation pp. 440 - 445
• Main Authors: Shane Dixon, Xiao-Hua Yu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2010
• Subjects: Artificial immune systems; Artificial neural networks; Bioinformatics; Data mining; Information analysis; Multi-layer neural network; Neural networks; Pattern analysis; Real-valued negative selection algorithm; Research and development
• ISBN: 1424457017; 9781424457014
• DOI: 10.1109/ICINFA.2010.5512376

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.