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| computational immunology ： ウィキペディア英語版 |
computational immunology
In academia, computational immunology is a field of science that encompasses high-throughput genomic and bioinformatics approaches to immunology. The field's main aim is to convert immunological data into computational problems, solve these problems using mathematical and computational approaches and then convert these results into immunologically meaningful interpretations.
== Introduction ==
The immune system is a complex system of the human body and understanding it is one of the most challenging topics in biology. Immunology research is important for understanding the mechanisms underlying the defense of human body and to develop drugs for immunological diseases and maintain health. Recent findings in genomic and proteomic technologies have transformed the immunology research drastically. Sequencing of the human and other model organism genomes has produced increasingly large volumes of data relevant to immunology research and at the same time huge amounts of functional and clinical data are being reported in the scientific literature and stored in clinical records. Recent advances in bioinformatics or computational biology were helpful to understand and organize these large scale data and gave rise to new area that is called ''Computational immunology'' or ''immunoinformatics''.
Computational immunology is a branch of bioinformatics and it is based on similar concepts and tools, such as sequence alignment and protein structure prediction tools. Immunomics is a discipline like genomics and proteomics. It is a science, which specifically combines Immunology with computer science, mathematics, chemistry, and biochemistry for large-scale analysis of immune system functions. It aims to study the complex protein–protein interactions and networks and allows a better understanding of immune responses and their role during normal, diseased and reconstitution states. Computational immunology is a part of immunomics, which is focused on analyzing large scale experimental data.
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