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2016-01-05

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cc-by (c) Tényi, Á. et al., 2016
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/68878

ChainRank, a chain prioritisation method for contextualisation of biological networks

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http://dx.doi.org/10.1186/s12859-015-0864-x

Abstract

Advances in high throughput technologies and growth of biomedical knowledge have contributed to an exponential increase in associative data. These data can be represented in the form of complex networks of biological associations, which are suitable for systems analyses. However, these networks usually lack both, context specificity in time and space as well as the distinctive borders, which are usually assigned in the classical pathway view of molecular events (e.g. signal transduction). This complexity and high interconnectedness call for automated techniques that can identify smaller targeted subnetworks specific to a given research context (e.g. a disease scenario).

Subject

Bioinformàtica, Biologia computacional, Proteïnes, Sistemes biològics

Subject (English)

Bioinformatics, Computational biology, Proteins, Biological systems

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Articles publicats en revistes (Bioquímica i Biomedicina Molecular)
Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)
Publicacions de projectes de recerca finançats per la UE

Citation

TÉNYI, Ákos, et al. ChainRank, a chain prioritisation method for contextualisation of biological networks. Bmc Bioinformatics. 2016. Vol. 17, num. 1, pags. 1-17. ISSN 1471-2105. [consulted: 9 of August of 2026]. Available at: https://hdl.handle.net/2445/68878

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