Bioinformatics data mining using artificial immune systems and neural networks

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 immun...

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Bibliographic Details
Published in2010 International Conference on Information and Automation pp. 440 - 445
Main Authors Shane Dixon, Xiao-Hua Yu
Format Conference Proceeding
LanguageEnglish
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
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ISBN1424457017
9781424457014
DOI10.1109/ICINFA.2010.5512376

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Summary: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.
ISBN:1424457017
9781424457014
DOI:10.1109/ICINFA.2010.5512376