Bergherr, Elisabeth

[["dc.bibliographiccitation.firstpage","77"],["dc.bibliographiccitation.journal","Frontiers in Neuroscience"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Salvi, Rachele"],["dc.contributor.author","Steigleder, Tobias"],["dc.contributor.author","Schlachetzki, Johannes C. M."],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Schwab, Stefan"],["dc.contributor.author","Winner, Beate"],["dc.contributor.author","Winkler, Jürgen"],["dc.contributor.author","Kohl, Zacharias"],["dc.date.accessioned","2021-09-17T09:11:00Z"],["dc.date.available","2021-09-17T09:11:00Z"],["dc.date.issued","2016"],["dc.description.abstract","While adult neurogenesis is considered to be restricted to the hippocampal dentate gyrus (DG) and the subventricular zone (SVZ), recent studies in humans and rodents provide evidence for newly generated neurons in regions generally considered as non-neurogenic, e.g., the striatum. Stimulating dopaminergic neurotransmission has the potential to enhance adult neurogenesis in the SVZ and the DG most likely via D2/D3 dopamine (DA) receptors. Here, we investigated the effect of two distinct preferential D2/D3 DA agonists, Pramipexole (PPX), and Ropinirole (ROP), on adult neurogenesis in the hippocampus and striatum of adult naïve mice. To determine newly generated cells in the DG incorporating 5-bromo-2'-deoxyuridine (BrdU) a proliferation paradigm was performed in which two BrdU injections (100 mg/kg) were applied intraperitoneally within 12 h after a 14-days-DA agonist treatment. Interestingly, PPX, but not ROP significantly enhanced the proliferation in the DG by 42% compared to phosphate buffered saline (PBS)-injected control mice. To analyze the proportion of newly generated cells differentiating into mature neurons, we quantified cells co-expressing BrdU and Neuronal Nuclei (NeuN) 32 days after the last of five BrdU injections (50 mg/kg) applied at the beginning of 14-days DA agonist or PBS administration. Again, PPX only enhanced neurogenesis in the DG significantly compared to ROP- and PBS-injected mice. Moreover, we explored the pro-neurogenic effect of both DA agonists in the striatum by quantifying neuroblasts expressing doublecortin (DCX) in the entire striatum, as well as in the dorsal and ventral sub-regions separately. We observed a significantly higher number of DCX(+) neuroblasts in the dorsal compared to the ventral sub-region of the striatum in PPX-injected mice. These results suggest that the stimulation of hippocampal and dorsal striatal neurogenesis may be up-regulated by PPX. The increased generation of neural cells, both in constitutively active and quiescent neurogenic niches, might be related to the proportional higher D3 receptor affinity of PPX, non-dopaminergic effects of PPX, or altered motor behavior."],["dc.identifier.doi","10.3389/fnins.2016.00077"],["dc.identifier.pmid","27013940"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89627"],["dc.language.iso","en"],["dc.relation.issn","1662-4548"],["dc.title","Distinct Effects of Chronic Dopaminergic Stimulation on Hippocampal Neurogenesis and Striatal Doublecortin Expression in Adult Mice"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1104-1121"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Biometrical journal. Biometrische Zeitschrift"],["dc.bibliographiccitation.lastpage","1121"],["dc.bibliographiccitation.volume","59"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Taylor-Robinson, David"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Pressler, Tania"],["dc.contributor.author","Schmid, Matthias"],["dc.contributor.author","Mayr, Andreas"],["dc.date.accessioned","2018-03-13T15:00:13Z"],["dc.date.available","2018-03-13T15:00:13Z"],["dc.date.issued","2017"],["dc.description.abstract","Joint models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes should thus be modeled jointly in order to prevent the potential bias introduced by independent modeling. Commonly, joint models are estimated in likelihood-based expectation maximization or Bayesian approaches using frameworks where variable selection is problematic and that do not immediately work for high-dimensional data. In this paper, we propose a boosting algorithm tackling these challenges by being able to simultaneously estimate predictors for joint models and automatically select the most influential variables even in high-dimensional data situations. We analyze the performance of the new algorithm in a simulation study and apply it to the Danish cystic fibrosis registry that collects longitudinal lung function data on patients with cystic fibrosis together with data regarding the onset of pulmonary infections. This is the first approach to combine state-of-the art algorithms from the field of machine-learning with the model class of joint models, providing a fully data-driven mechanism to select variables and predictor effects in a unified framework of boosting joint models."],["dc.identifier.doi","10.1002/bimj.201600158"],["dc.identifier.pmid","28321912"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/13023"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.relation.eissn","1521-4036"],["dc.title","Boosting joint models for longitudinal and time-to-event data"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","4041736"],["dc.bibliographiccitation.journal","Computational and Mathematical Methods in Medicine"],["dc.bibliographiccitation.volume","2017"],["dc.contributor.author","Gefeller, Olaf"],["dc.contributor.author","Hofner, Benjamin"],["dc.contributor.author","Mayr, Andreas"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.date.accessioned","2021-09-17T09:10:26Z"],["dc.date.available","2021-09-17T09:10:26Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1155/2017/4041736"],["dc.identifier.pmid","29093744"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89621"],["dc.language.iso","en"],["dc.relation.eissn","1748-6718"],["dc.relation.issn","1748-670X"],["dc.title","Predictive Modelling Based on Statistical Learning in Biomedicine"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","422"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Methods of Information in Medicine"],["dc.bibliographiccitation.lastpage","430"],["dc.bibliographiccitation.volume","55"],["dc.contributor.author","Hepp, Tobias"],["dc.contributor.author","Schmid, Matthias"],["dc.contributor.author","Gefeller, Olaf"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Mayr, Andreas"],["dc.date.accessioned","2021-09-17T09:11:09Z"],["dc.date.available","2021-09-17T09:11:09Z"],["dc.date.issued","2016-10-17"],["dc.description.abstract","Penalization and regularization techniques for statistical modeling have attracted increasing attention in biomedical research due to their advantages in the presence of high-dimensional data. A special focus lies on algorithms that incorporate automatic variable selection like the least absolute shrinkage operator (lasso) or statistical boosting techniques."],["dc.identifier.doi","10.3414/ME16-01-0033"],["dc.identifier.pmid","27626931"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89629"],["dc.language.iso","en"],["dc.relation.eissn","2511-705X"],["dc.relation.issn","0026-1270"],["dc.title","Approaches to Regularized Regression - A Comparison between Gradient Boosting and the Lasso"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","886"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Cancer"],["dc.bibliographiccitation.volume","18"],["dc.contributor.author","Schink, Kristin"],["dc.contributor.author","Herrmann, Hans J."],["dc.contributor.author","Schwappacher, Raphaela"],["dc.contributor.author","Meyer, Julia"],["dc.contributor.author","Orlemann, Till"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Wullich, Bernd"],["dc.contributor.author","Kahlmeyer, Andreas"],["dc.contributor.author","Fietkau, Rainer"],["dc.contributor.author","Lubgan, Dorota"],["dc.contributor.author","Beckmann, Matthias W."],["dc.contributor.author","Hack, Carolin"],["dc.contributor.author","Kemmler, Wolfgang"],["dc.contributor.author","Siebler, Jürgen"],["dc.contributor.author","Neurath, Markus F."],["dc.contributor.author","Zopf, Yurdagül"],["dc.date.accessioned","2021-09-17T09:10:32Z"],["dc.date.available","2021-09-17T09:10:32Z"],["dc.date.issued","2018-09-12"],["dc.description.abstract","Physical exercise and nutritional treatment are promising measures to prevent muscle wasting that is frequently observed in advanced-stage cancer patients. However, conventional exercise is not always suitable for these patients due to physical weakness and therapeutic side effects. In this pilot study, we examined the effect of a combined approach of the novel training method whole-body electromyostimulation (WB-EMS) and individualized nutritional support on body composition with primary focus on skeletal muscle mass in advanced cancer patients under oncological treatment."],["dc.identifier.doi","10.1186/s12885-018-4790-y"],["dc.identifier.pmid","30208857"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89622"],["dc.language.iso","en"],["dc.relation.issn","1471-2407"],["dc.title","Effects of whole-body electromyostimulation combined with individualized nutritional support on body composition in patients with advanced cancer: a controlled pilot trial"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","23"],["dc.bibliographiccitation.journal","Frontiers in Psychiatry"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Scheel, Jennifer Felicia"],["dc.contributor.author","Schieber, Katharina"],["dc.contributor.author","Reber, Sandra"],["dc.contributor.author","Stoessel, Lisa"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Jank, Sabine"],["dc.contributor.author","Eckardt, Kai-Uwe"],["dc.contributor.author","Grundmann, Franziska"],["dc.contributor.author","Vitinius, Frank"],["dc.contributor.author","de Zwaan, Martina"],["dc.contributor.author","Bertram, Anna"],["dc.contributor.author","Erim, Yesim"],["dc.date.accessioned","2021-09-17T09:11:05Z"],["dc.date.available","2021-09-17T09:11:05Z"],["dc.date.issued","2018"],["dc.description.abstract","Non-adherence to immunosuppressive medication is regarded as an important factor for graft rejection and loss after successful renal transplantation. Yet, results on prevalence and relationship with psychosocial parameters are heterogeneous. The main aim of this study was to investigate the association of immunosuppressive medication non-adherence and psychosocial factors."],["dc.identifier.doi","10.3389/fpsyt.2018.00023"],["dc.identifier.pmid","29497386"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89628"],["dc.language.iso","en"],["dc.relation.issn","1664-0640"],["dc.title","Psychosocial Variables Associated with Immunosuppressive Medication Non-Adherence after Renal Transplantation"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","326"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Statistical Modelling"],["dc.bibliographiccitation.lastpage","344"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2022-06-08T07:57:14Z"],["dc.date.available","2022-06-08T07:57:14Z"],["dc.date.issued","2014"],["dc.description.abstract","Quantile regression (QR) has become a widely used tool to study the impact of covariates on quantiles of a response distribution. QR provides a detailed description of the conditional response when considering a dense set of quantiles, without assuming a closed form for its distribution. The Bayesian version of QR, which can be implemented by considering the asymmetric Laplace distribution (ALD) as an auxiliary error distribution, is an attractive alternative to other methods because it returns knowledge on the whole parameter distribution instead of solely point estimations. While for the univariate case there has been a lot of development in the last few years, multivariate responses have only been treated to a little extent in the literature, especially in the Bayesian case. By using a multivariate version of the location scale mixture representation for the ALD, we are able to apply inference techniques developed for multivariate Gaussian models on multivariate quantile regression and make thus the impact of covariates on the quantiles of more than one dependent variable feasible. The model structure also facilitates the determination of conditional correlations between bivariate responses on different quantile levels after adjusting for covariate effects."],["dc.identifier.doi","10.1177/1471082x14551247"],["dc.identifier.gro","3149375"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110033"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6045"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","SAGE Publications"],["dc.relation.eissn","1477-0342"],["dc.relation.issn","1471-082X"],["dc.rights.uri","http://journals.sagepub.com/page/policies/text-and-data-mining-license"],["dc.title","Bayesian bivariate quantile regression"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1539"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Statistics and Computing"],["dc.bibliographiccitation.lastpage","1553"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Sobotka, Fabian"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2018-03-13T15:04:18Z"],["dc.date.available","2018-03-13T15:04:18Z"],["dc.date.issued","2016"],["dc.identifier.doi","10.1007/s11222-016-9703-9"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/13024"],["dc.notes.status","zu prüfen"],["dc.title","Bayesian regularisation in geoadditive expectile regression"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","6083072"],["dc.bibliographiccitation.journal","Computational and Mathematical Methods in Medicine"],["dc.contributor.author","Mayr, Andreas"],["dc.contributor.author","Hofner, Benjamin"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Hepp, Tobias"],["dc.contributor.author","Meyer, Sebastian"],["dc.contributor.author","Gefeller, Olaf"],["dc.date.accessioned","2021-09-17T09:10:53Z"],["dc.date.available","2021-09-17T09:10:53Z"],["dc.date.issued","2017"],["dc.description.abstract","Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression, and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine."],["dc.identifier.doi","10.1155/2017/6083072"],["dc.identifier.pmid","28831290"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89626"],["dc.language.iso","en"],["dc.relation.eissn","1748-6718"],["dc.relation.issn","1748-670X"],["dc.title","An Update on Statistical Boosting in Biomedicine"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","317"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","The International Journal of Biostatistics"],["dc.bibliographiccitation.lastpage","329"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","Griesbach, Colin"],["dc.contributor.author","Säfken, Benjamin"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.date.accessioned","2022-03-23T09:52:10Z"],["dc.date.available","2022-03-23T09:52:10Z"],["dc.date.issued","2021"],["dc.description.abstract","Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by adapting concepts from classification theory. Current boosting approaches also offer methods accounting for random effects and thus enable prediction of mixed models for longitudinal and clustered data. However, these approaches include several flaws resulting in unbalanced effect selection with falsely induced shrinkage and a low convergence rate on the one hand and biased estimates of the random effects on the other hand. We therefore propose a new boosting algorithm which explicitly accounts for the random structure by excluding it from the selection procedure, properly correcting the random effects estimates and in addition providing likelihood-based estimation of the random effects variance structure. The new algorithm offers an organic and unbiased fitting approach, which is shown via simulations and data examples."],["dc.identifier.doi","10.1515/ijb-2020-0136"],["dc.identifier.pmid","34826371"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/105070"],["dc.language.iso","en"],["dc.relation.eissn","1557-4679"],["dc.relation.orgunit","Professur für Raumbezogene Datenanalyse und Statistische Lernverfahren"],["dc.title","Gradient boosting for linear mixed models"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]