Academic Journal

Improving the reuse of computational models through version control.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Improving the reuse of computational models through version control.
Συγγραφείς: Waltemath, Dagmar, Henkel, Ron, Hälke, Robert, Scharm, Martin, Wolkenhauer, Olaf
Πηγή: Bioinformatics; Mar2013, Vol. 29 Issue 6, p742-748, 7p
Θεματικοί όροι: MODEL (Computer program language), XML (Extensible Markup Language), RDF (Document markup language), Computer software development, Biochemical models
Περίληψη: Motivation: Only models that are accessible to researchers can be reused. As computational models evolve over time, a number of different but related versions of a model exist. Consequently, tools are required to manage not only well-curated models but also their associated versions.Results: In this work, we discuss conceptual requirements for model version control. Focusing on XML formats such as Systems Biology Markup Language and CellML, we present methods for the identification and explanation of differences and for the justification of changes between model versions. In consequence, researchers can reflect on these changes, which in turn have considerable value for the development of new models. The implementation of model version control will therefore foster the exploration of published models and increase their reusability.Availability: We have implemented the proposed methods in a software library called Biochemical Model Version Control System. It is freely available at http://sems.uni-rostock.de/bives/. Biochemical Model Version Control System is also integrated in the online application BudHat, which is available for testing at http://sems.uni-rostock.de/budhat/ (The version described in this publication is available from http://budhat-demo.sems.uni-rostock.de/).Contact: dagmar.waltemath@uni-rostock.de [ABSTRACT FROM AUTHOR]
Copyright of Bioinformatics is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
Περιγραφή
ISSN:13674803
DOI:10.1093/bioinformatics/btt018