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Artificial Immune Networks as a Paradigm for Classification and Profiling of Gene Expression Data

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The paper introduces a methodology of using artificial immune network systems (AINS) for classification and in particular—classification of gene expression data. AINS are computational models that adopt principles from biological immune systems. Two types of AINS are explored in the paper. For an illustrative case study we have used publicly available gene expression data of two types of Lymphoma—DLBCL and FL, and also diffuse large B-cell lymphoma (DLBCL) patient survival data after chemotherapy (Shipp et al., 2002). The results demonstrate the applicability of the AINS as classifiers in biomedical decision support systems.
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Document Type: Research Article

Publication date: 2005-12-01

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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