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Review
. 2003;254:23-32; discussion 33-42, 98-101, 250-2.

The Future for Computational Modelling and Prediction Systems in Clinical Immunology

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  • PMID: 14712930
Review

The Future for Computational Modelling and Prediction Systems in Clinical Immunology

Nikolai Petrovsky et al. Novartis Found Symp. .

Abstract

Advances in computational science, despite their enormous potential, have been surprisingly slow to impact on clinical practice. This paper examines the potential of bioinformatics to advance clinical immunology across a number of key examples including the use of computational immunology to improve renal transplantation outcomes, identify novel genes involved in immunological disorders, decipher the relationship between antigen presentation pathways and human disease, and predict allergenicity. These examples demonstrate the enormous potential for immunoinformatics to advance clinical and experimental immunology. The acceptance of immunoinformatic techniques by clinical and research immunologists will need robust standards of data quality, system integrity and properly validated immunoinformatic systems. Such validation, at a minimum, will require appropriately designed clinical studies conducted according to Good Clinical Practice standards. This strategy will enable immunoinformatics to achieve its full potential to advance and shape clinical immunology in the future.

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