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Originalartikel | erschienen - EPub | peer reviewed | Open Access

SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks


BRIEFINGS IN BIOINFORMATICS 2021 ;






Bibliometrische Indikatoren



Impact Factor = 13,994

Zitierhäufigkeit nach WOS = 0

DOI = https://doi.org/10.1093/bib/bbab049


Autoren

Rougny A*, Touré V, Albanese J, Waltemath D1, Shirshov D, Sorokin A, Bader G, Blinov M, Mazein A


Abstract

A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the BricKs Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org.

Veröffentlicht in

BRIEFINGS IN BIOINFORMATICS


Jahr 2021
Impact Factor (2021) 13,994
Volume
Issue
Seiten -
Open Access ja
Peer reviewed ja
Artikelart Originalartikel
Artikelstatus erschienen - EPub
DOI https://doi.org/10.1093/bib/bbab049

Allgemeine Daten zur Fachzeitschrift

Kurzbezeichnung: BRIEF BIOINFORM
ISSN: 1467-5463
eISSN: 1477-4054
Land: ENGLAND
Sprache: English
Kategorie(n):
  • MATHEMATICAL & COMPUTATIONAL BIOLOGY
  • BIOCHEMICAL RESEARCH METHODS


Impact Factor Entwicklung

Jahr Impact Factor
2008 4,627
2009 7,329
2010 9,283
2011 5,202
2012 5,298
2013 5,919
2014 9,617
2015 8,399
2016 5,134
2017 6,302
2018 9,101
2019 8,99
2020 11,622
2021 13,994
2022 9,5
2023 6,8
2024 7,7

Forschungsschwerpunkt der Universität



Beteiligte Departments

Data Science


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