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Oana Chis

dblp:84/10115 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › systems biology › parameter estimation
structural identifiability analysis
0.522018
GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models · Bioinform. 2018
GenSSI: a software toolbox for structural identifiability analysis of biological models · Bioinform. 2011
Bioinformatics and computational biology
systems biology
0.312018
GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models · Bioinform. 2018
Bioinformatics and computational biology › systems biology › computational systems biology
systems biology modeling
0.112011
GenSSI: a software toolbox for structural identifiability analysis of biological models · Bioinform. 2011

Methods — techniques the papers use, named apart from their topics

state/parameter transformation · 0.3generating series approach · 0.3nonlinear dynamic model analysis · 0.1generating series · 0.1
YearPublicationVenuePosition
2018 GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models
abstract
Motivation: Mathematical modeling using ordinary differential equations is used in systems biology to improve the understanding of dynamic biological processes. The parameters of ordinary differential equation models are usually estimated from experimental data. To analyze a priori the uniqueness of the solution of the estimation problem, structural identifiability analysis methods have been developed. Results: We introduce GenSSI 2.0, an advancement of the software toolbox GenSSI (Generating Series for testing Structural Identifiability). GenSSI 2.0 is the first toolbox for structural identifiability analysis to implement Systems Biology Markup Language import, state/parameter transformations and multi-experiment structural identifiability analysis. In addition, GenSSI 2.0 supports a range of MATLAB versions and is computationally more efficient than its previous version, enabling the analysis of more complex models. Availability and implementation: GenSSI 2.0 is an open-source MATLAB toolbox and available at https://github.com/genssi-developer/GenSSI. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.
Thomas S. Ligon, Fabian Fröhlich, Oana Chis, Julio R. Banga, Eva Balsa-Canto, Jan Hasenauer
Bioinform.3
2011 GenSSI: a software toolbox for structural identifiability analysis of biological models
abstract
SUMMARY: Mathematical modeling has a key role in systems biology. Model building is often regarded as an iterative loop involving several tasks, among which the estimation of unknown parameters of the model from a certain set of experimental data is of central importance. This problem of parameter estimation has many possible pitfalls, and modelers should be very careful to avoid them. Many of such difficulties arise from a fundamental (yet often overlooked) property: the so-called structural (or a priori) identifiability, which considers the uniqueness of the estimated parameters. Obviously, the structural identifiability of any tentative model should be checked at the beginning of the model building loop. However, checking this property for arbitrary non-linear dynamic models is not an easy task. Here we present a software toolbox, GenSSI (Generating Series for testing Structural Identifiability), which enables non-expert users to carry out such analysis. The toolbox runs under the popular MATLAB environment and is accompanied by detailed documentation and relevant examples. AVAILABILITY: The GenSSI toolbox and the related documentation are available at http://www.iim.csic.es/%7Egenssi. CONTACT: [email protected].
Oana Chis, Julio R. Banga, Eva Balsa-Canto
Bioinform.1