Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
The impact of multiple T cell-APC encounters and the role of energy
Journal of Computational and Applied Mathematics - Special issue: Mathematics applied to immunology
Structural design of the danger model immune algorithm
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
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Artificial Immune Systems are engineering systems which have been inspired from the functioning of the biological immune system. We present an immune system model which incorporates two biologically motivated mechanisms to protect against autoimmune reactions, or false positives. The first, anergy, has been subject to the intense focus of immunologists as a possible key to autoimmune disease. The second is danger theory, which has attracted much interest as a possible alternative to traditional self-nonself selection models.We adopt a published immunological model, validate and extend it. Using the same calculations and assumptions as the original model, we integrate danger theory into the software.Without anergy, both models - the original and the danger model - produce similar results. When anergy is added, both models' performance improves. However, there seems to be some synergy between the mechanisms; anergy has a greater effect on the danger model than the original model. These findings should be of interest both to AIS practitioners and to the immunological community.