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Marianna Grinberg

dblp:424/3503 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

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
1 paper
Bioinformatics and computational biology · 50% Medical and health informatics · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics
dose-response modeling
0.512021
Comparison of observation-based and model-based identification of alert concentrations from concentration-expression data · Bioinform. 2021
Bioinformatics and computational biology
gene expression analysis
0.512021
Comparison of observation-based and model-based identification of alert concentrations from concentration-expression data · Bioinform. 2021

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

simulation study · 0.5parametric curve fitting · 0.5
YearPublicationVenuePosition
2021 Comparison of observation-based and model-based identification of alert concentrations from concentration-expression data
abstract
MOTIVATION: An important goal of concentration-response studies in toxicology is to determine an 'alert' concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest. RESULTS: In a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance. AVAILABILITY AND IMPLEMENTATION: The code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Franziska Kappenberg, Marianna Grinberg, Xiaoqi Jiang, Annette Kopp-Schneider, Jan G. Hengstler, Jörg Rahnenführer
Bioinform.2