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Procedia Computer Science
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Procedia Computer Science
Article . 2016
License: CC BY NC ND
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Logical and Semantic Modeling of Complex Biomolecular Networks

Authors: Ayadi, Ali; Zanni-Merk, Cecilia; de Bertrand de Beuvron, Francois; Krichen, Saoussen;

Logical and Semantic Modeling of Complex Biomolecular Networks

Abstract

AbstractSystems biology models aim to describe and understand the behaviour of a cell. This living organism is represented by a complex biomolecular network. In the literature, most researches focus only on modeling isolated parts of this network, such as the metabolic network or the gene regulatory network. However, to fully understand the behaviour of a cell we should model and analyze the biomolecular network as a whole.Towards this goal, we firstly present a formalization for describing the logical structure, function and behaviour of complex biomolecular networks. In addition, we propose a semantic approach based on four ontologies to provide a rich description for modeling a biomolecular network and its state changes. This approach contributes to propose to the biologist a platform where to simulate the state changes of biomolecular networks with the hope of steering their behaviours.

Country
France
Keywords

Complex biomolecular networks, [SPI.OTHER] Engineering Sciences [physics]/Other, Transittability, Logical modeling, Informatique [cs]/Apprentissage [cs.LG], Semantic approach

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    popularity
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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Top 10%
Green
gold