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Application of information theory in systems biology

Over recent years, new light has been shed on aspects of information processing in cells. The quantification of information, as described by Shannon’s information theory, is a basic and powerful tool that can be applied to various fields, such as communication, statistics, and computer science, as w...

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Autor principal: Uda, Shinsuke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242537/
https://www.ncbi.nlm.nih.gov/pubmed/32144740
http://dx.doi.org/10.1007/s12551-020-00665-w
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author Uda, Shinsuke
author_facet Uda, Shinsuke
author_sort Uda, Shinsuke
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description Over recent years, new light has been shed on aspects of information processing in cells. The quantification of information, as described by Shannon’s information theory, is a basic and powerful tool that can be applied to various fields, such as communication, statistics, and computer science, as well as to information processing within cells. It has also been used to infer the network structure of molecular species. However, the difficulty of obtaining sufficient sample sizes and the computational burden associated with the high-dimensional data often encountered in biology can result in bottlenecks in the application of information theory to systems biology. This article provides an overview of the application of information theory to systems biology, discussing the associated bottlenecks and reviewing recent work.
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spelling pubmed-72425372020-06-03 Application of information theory in systems biology Uda, Shinsuke Biophys Rev Review Over recent years, new light has been shed on aspects of information processing in cells. The quantification of information, as described by Shannon’s information theory, is a basic and powerful tool that can be applied to various fields, such as communication, statistics, and computer science, as well as to information processing within cells. It has also been used to infer the network structure of molecular species. However, the difficulty of obtaining sufficient sample sizes and the computational burden associated with the high-dimensional data often encountered in biology can result in bottlenecks in the application of information theory to systems biology. This article provides an overview of the application of information theory to systems biology, discussing the associated bottlenecks and reviewing recent work. Springer Berlin Heidelberg 2020-03-06 /pmc/articles/PMC7242537/ /pubmed/32144740 http://dx.doi.org/10.1007/s12551-020-00665-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Review
Uda, Shinsuke
Application of information theory in systems biology
title Application of information theory in systems biology
title_full Application of information theory in systems biology
title_fullStr Application of information theory in systems biology
title_full_unstemmed Application of information theory in systems biology
title_short Application of information theory in systems biology
title_sort application of information theory in systems biology
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242537/
https://www.ncbi.nlm.nih.gov/pubmed/32144740
http://dx.doi.org/10.1007/s12551-020-00665-w
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