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| 024 | 7 | _ | |a 10.1093/bioinformatics/btab043 |2 doi |
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| 037 | _ | _ | |a DKFZ-2021-00239 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 570 |
| 100 | 1 | _ | |a Kappenberg, Franziska |b 0 |
| 245 | _ | _ | |a Comparison Of Observation-Based And Model-Based Identification Of Alert Concentrations From Concentration-Expression Data. |
| 260 | _ | _ | |a Oxford |c 2021 |b Oxford Univ. Press |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a 2021 Jan 30;37(14):1990-1996 |
| 520 | _ | _ | |a 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.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.The code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations.Supplementary data are available at Bioinformatics online. |
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| 700 | 1 | _ | |a Grinberg, Marianna |b 1 |
| 700 | 1 | _ | |a Jiang, Xiaoqi |b 2 |
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| 700 | 1 | _ | |a Hengstler, Jan G |b 4 |
| 700 | 1 | _ | |a Rahnenführer, Jörg |b 5 |
| 773 | _ | _ | |a 10.1093/bioinformatics/btab043 |g p. btab043 |0 PERI:(DE-600)1468345-3 |n 14 |p 1990-1996 |t Bioinformatics |v 37 |y 2021 |x 1460-2059 |
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