Artificial Immune Systems in Bioinformatics

Abstract

Summary. Artificial Immune Systems (AIS) represent one of the most recent and promising approaches in the branch of bio-inspired techniques. Although this open field of research is still in its infancy, several relevant results have been achieved by using the AIS paradigm in demanding tasks such as the ones coming from computational biology and biochemistry. The chapter will show how AIS have been successfully used in computational biology problems and will give readers further hints about possible implementations in unexplored fields. The main goal of the contribution lays in providing both theoretical foundations and hands-on experience that allow researchers to figure out novel applications of AIS in bioinformatics and, at the same time, providing researchers with necessary insights for implementation in daily research. The contribution will be organised in 5 sections. IntroductionArtificial Immune Systems (AIS) represent one of the most recent and promising approaches in the branch of bio-inspired techniques. Although this open field of research is still in its infancy, several relevant results have been achieved by using the AIS paradigm in demanding tasks such as the ones coming from computational biology and biochemistry. Artificial immune systems (AIS) can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. Their development and application domains follow those of soft computing paradigms such as artificial neural networks (ANN), evolutionary algorithms (EA) and fuzzy systems (FS). Soft computing was the term coined to address a new trend of coexistence and integration that reflects a high degree of interaction among several computational intelligence approaches like artificial neural network, evolutionary

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