Title :
Bioinformatics data mining using artificial immune systems and neural networks
Author :
Dixon, Shane ; Yu, Xiao-Hua
Author_Institution :
Dept. of Electr. Eng., California Polytech. State Univ., San Luis Obispo, CA, USA
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.
Keywords :
artificial immune systems; bioinformatics; data mining; multilayer perceptrons; artificial immune systems; artificial neural networks; bioinformatics data mining; biomedical analysis; multilayer feedforward neural network model; Artificial immune systems; Artificial neural networks; Bioinformatics; Data mining; Databases; Information analysis; Multi-layer neural network; Neural networks; Pattern analysis; Research and development; Artificial immune systems; Artificial neural networks; Data mining; Real-valued negative selection algorithm;
Conference_Titel :
Information and Automation (ICIA), 2010 IEEE International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-5701-4
DOI :
10.1109/ICINFA.2010.5512376