Prof. Dr. Sonja Greven
Profil
Forschungsthemen7
Flexible Dichteregressionsmethoden
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 10/2023 - 09/2026 Projektleitung: Prof. Dr. Sonja Greven
Flexible Regressionsmethoden für Kurven and Formen
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 03/2026 - 03/2029 Projektleitung: Prof. Dr. Sonja Greven
Flexible Regressionsmethoden für Kurven und Formen
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 01/2020 - 01/2023 Projektleitung: Prof. Dr. Sonja Greven
FOR 5363: KI-FOR Integration von Deep Learning und Statistik zum Verständnis strukturierter biomedizinischer Daten
Quelle ↗Förderer: DFG Forschungsgruppe Zeitraum: 01/2023 - 01/2027 Projektleitung: Prof. Dr. Sonja Greven
SFB/TRR 190/2: Informationsinfrastrukturprojekt (SP INF)
Quelle ↗Förderer: DFG Sonderforschungsbereich Zeitraum: 01/2021 - 12/2024 Projektleitung: Prof. Dr. Sonja Greven
Statistische Modellierung unter Verwendung von Mausbewegungen zur Modellierung von Messfehlern und zur Verbesserung der Datenqualität in Web Surveys
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 05/2024 - 04/2027 Projektleitung: Prof. Dr. Sonja Greven
Statistische Modellierung unter Verwendung von Mausbewegungen zur Modellierung von Messfehlern und zur Verbesserung der Datenqualität in Web Surveys
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 12/2018 - 07/2024 Projektleitung: Prof. Dr. Sonja Greven
Mögliche Industrie-Partner10
Stand: 26.4.2026, 19:48:44 (Top-K=20, Min-Cosine=0.4)
- 87 Treffer59.6%
- Workshop Reliable Methods and Mathematical ModelingP59.6%
- Workshop Reliable Methods and Mathematical Modeling
- 16 Treffer57.4%
- Zuwendung im Rahmen des Programms „exist – Existenzgründungen aus der Wissenschaft“ aus dem Bundeshaushalt, Einzelplan 09, Kapitel 02, Titel 68607, Haushaltsjahr 2026, sowie aus Mitteln des Europäischen Strukturfonds (hier Euro-päischer Sozialfonds Plus – ESF Plus) Förderperiode 2021-2027 – Kofinanzierung für das Vorhaben: „exist Women“T57.4%
- Zuwendung im Rahmen des Programms „exist – Existenzgründungen aus der Wissenschaft“ aus dem Bundeshaushalt, Einzelplan 09, Kapitel 02, Titel 68607, Haushaltsjahr 2026, sowie aus Mitteln des Europäischen Strukturfonds (hier Euro-päischer Sozialfonds Plus – ESF Plus) Förderperiode 2021-2027 – Kofinanzierung für das Vorhaben: „exist Women“
- 26 Treffer57.4%
- Design & Implementierung eines neuronalen Netzwerks für die Personendetektion (Transferbonus)P57.4%
- Design & Implementierung eines neuronalen Netzwerks für die Personendetektion (Transferbonus)
- 20 Treffer55.6%
- Embodied Audition for RobotSP55.6%
- Embodied Audition for RobotS
- 27 Treffer55.1%
- Systematic Models for Biological Systems Engineering Training NetworkP55.1%
- Systematic Models for Biological Systems Engineering Training Network
Protatuans-Etaireia Ereynas Viotechologias Monoprosopi Etaireia Periorisments Eythinis
P24 Treffer55.1%- Systematic Models for Biological Systems Engineering Training NetworkP55.1%
- Systematic Models for Biological Systems Engineering Training Network
- 26 Treffer55.1%
- Systematic Models for Biological Systems Engineering Training NetworkP55.1%
- Systematic Models for Biological Systems Engineering Training Network
- 25 Treffer55.1%
- Systematic Models for Biological Systems Engineering Training NetworkP55.1%
- Systematic Models for Biological Systems Engineering Training Network
- 20 Treffer54.8%
- Bewertung der physiologischen Plastizität und genetischen Variabilität der Kiefer (Pinus sylvestris L.) an ihrer westlichen Verbreitungsgrenze unter den Bedingungen des KlimawandelsP54.8%
- Bewertung der physiologischen Plastizität und genetischen Variabilität der Kiefer (Pinus sylvestris L.) an ihrer westlichen Verbreitungsgrenze unter den Bedingungen des Klimawandels
- 2 Treffer54.6%
- Satellitengestützte Information zur GrünlandbewirtschaftungP54.6%
- Satellitengestützte Information zur Grünlandbewirtschaftung
Publikationen25
Top 25 nach Zitationen — Quelle: OpenAlex (BAAI/bge-m3 embedded für Matching).
New England Journal of Medicine · 425 Zitationen · DOI
Viewing a stressful soccer match more than doubles the risk of an acute cardiovascular event. In view of this excess risk, particularly in men with known coronary heart disease, preventive measures are urgently needed.
Journal of the American Statistical Association · 346 Zitationen · DOI
Existing approaches for multivariate functional principal component analysis are restricted to data on the same one-dimensional interval. The presented approach focuses on multivariate functional data on different domains that may differ in dimension, such as functions and images. The theoretical basis for multivariate functional principal component analysis is given in terms of a Karhunen–Loève Theorem. For the practically relevant case of a finite Karhunen–Loève representation, a relationship between univariate and multivariate functional principal component analysis is established. This offers an estimation strategy to calculate multivariate functional principal components and scores based on their univariate counterparts. For the resulting estimators, asymptotic results are derived. The approach can be extended to finite univariate expansions in general, not necessarily orthonormal bases. It is also applicable for sparse functional data or data with measurement error. A flexible R implementation is available on CRAN. The new method is shown to be competitive to existing approaches for data observed on a common one-dimensional domain. The motivating application is a neuroimaging study, where the goal is to explore how longitudinal trajectories of a neuropsychological test score covary with FDG-PET brain scans at baseline. Supplementary material, including detailed proofs, additional simulation results, and software is available online.
Computational Statistics & Data Analysis · 332 Zitationen · DOI
Environmental Health Perspectives · 312 Zitationen · DOI
Results indicate an immediate response to PNC on the IL-6 level, possibly leading to the production of acute-phase proteins, as seen in increased fibrinogen levels. This might provide a link between air pollution and adverse cardiac events.
Biometrika · 259 Zitationen · DOI
Abstract In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion, aic, have been used, based either on the marginal or on the conditional distribution. We show that the marginal aic is not an asymptotically unbiased estimator of the Akaike information, and favours smaller models without random effects. For the conditional aic, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that can lead to the selection of any random effect not predicted to be exactly zero. We derive an analytic representation of a corrected version of the conditional aic, which avoids the high computational cost and imprecision of available numerical approximations. An implementation in an R package (R Development Core Team, 2010) is provided. All theoretical results are illustrated in simulation studies, and their impact in practice is investigated in an analysis of childhood malnutrition in Zambia.
Journal of Computational and Graphical Statistics · 237 Zitationen · DOI
We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible correlation structures for, e.g., spatial, temporal, or longitudinal functional data. Additionally, our framework includes linear and nonlinear effects of functional and scalar covariates that may vary smoothly over the index of the functional response. It accommodates densely or sparsely observed functional responses and predictors which may be observed with additional error and includes both spline-based and functional principal component-based terms. Estimation and inference in this framework is based on standard additive mixed models, allowing us to take advantage of established methods and robust, flexible algorithms. We provide easy-to-use open source software in the pffr() function for the R-package refund. Simulations show that the proposed method recovers relevant effects reliably, handles small sample sizes well and also scales to larger data sets. Applications with spatially and longitudinally observed functional data demonstrate the flexibility in modeling and interpretability of results of our approach.
Electronic Journal of Statistics · 181 Zitationen · DOI
We introduce models for the analysis of functional data observed at multiple time points. The dynamic behavior of functional data is decomposed into a time-dependent population average, baseline (or static) subject-specific variability, longitudinal (or dynamic) subject-specific variability, subject-visit-specific variability and measurement error. The model can be viewed as the functional analog of the classical longitudinal mixed effects model where random effects are replaced by random processes. Methods have wide applicability and are computationally feasible for moderate and large data sets. Computational feasibility is assured by using principal component bases for the functional processes. The methodology is motivated by and applied to a diffusion tensor imaging (DTI) study designed to analyze differences and changes in brain connectivity in healthy volunteers and multiple sclerosis (MS) patients. An R implementation is provided.87.
Journal of Computational and Graphical Statistics · 134 Zitationen · DOI
The goal of our article is to provide a transparent, robust, and computationally feasible statistical platform for restricted likelihood ratio testing (RLRT) for zero variance components in linear mixed models. This problem is nonstandard because under the null hypothesis the parameter is on the boundary of the parameter space. Our proposed approach is different from the asymptotic results of Stram and Lee who assumed that the outcome vector can be partitioned into many independent subvectors. Thus, our methodology applies to a wider class of mixed models, which includes models with a moderate number of clusters or nonparametric smoothing components. We propose two approximations to the finite sample null distribution of the RLRT statistic. Both approximations converge weakly to the asymptotic distribution obtained by Stram and Lee when their assumptions hold. When their assumptions do not hold, we show in extensive simulation studies that both approximations outperform the Stram and Lee approximation and the parametric bootstrap. We also identify and address numerical problems associated with standard mixed model software. Our methods are motivated by and applied to a large longitudinal study on air pollution health effects in a highly susceptible cohort. Relevant software is posted as an online supplement.
Computational Statistics · 132 Zitationen · DOI
Statistical Modelling · 130 Zitationen · DOI
Researchers are increasingly interested in regression models for functional data. This article discusses a comprehensive framework for additive (mixed) models for functional responses and/or functional covariates based on the guiding principle of reframing functional regression in terms of corresponding models for scalar data, allowing the adaptation of a large body of existing methods for these novel tasks. The framework encompasses many existing as well as new models. It includes regression for ‘generalized’ functional data, mean regression, quantile regression as well as generalized additive models for location, shape and scale (GAMLSS) for functional data. It admits many flexible linear, smooth or interaction terms of scalar and functional covariates as well as (functional) random effects and allows flexible choices of bases—particularly splines and functional principal components—and corresponding penalties for each term. It covers functional data observed on common (dense) or curve-specific (sparse) grids. Penalized-likelihood-based and gradient-boosting-based inference for these models are implemented in R packages refund and FDboost , respectively. We also discuss identifiability and computational complexity for the functional regression models covered. A running example on a longitudinal multiple sclerosis imaging study serves to illustrate the flexibility and utility of the proposed model class. Reproducible code for this case study is made available online.
Biometrics · 121 Zitationen · DOI
Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this article, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- and decomposition-based variability are constructed. Standard mixed model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. Iterated expectation and variance formulas combine model-based conditional estimates across the distribution of decompositions. A bootstrap procedure is implemented to understand the uncertainty in principal component decomposition quantities. Our method compares favorably to competing approaches in simulation studies that include both densely and sparsely observed functions. We apply our method to sparse observations of CD4 cell counts and to dense white-matter tract profiles. Code for the analyses and simulations is publicly available, and our method is implemented in the R package refund on CRAN.
An Approach to the Estimation of Chronic Air Pollution Effects Using Spatio-Temporal Information
2011Journal of the American Statistical Association · 65 Zitationen · DOI
There is substantial observational evidence that long-term exposure to particulate air pollution is associated with premature death in urban populations. Estimates of the magnitude of these effects derive largely from cross-sectional comparisons of adjusted mortality rates among cities with varying pollution levels. Such estimates are potentially confounded by other differences among the populations correlated with air pollution, for example, socioeconomic factors. An alternative approach is to study covariation of particulate matter and mortality across time within a city, as has been done in investigations of short-term exposures. In either event, observational studies like these are subject to confounding by unmeasured variables. Therefore the ability to detect such confounding and to derive estimates less affected by confounding are a high priority. In this article, we describe and apply a method of decomposing the exposure variable into components with variation at distinct temporal, spatial, and time by space scales, here focusing on the components involving time. Starting from a proportional hazard model, we derive a Poisson regression model and estimate two regression coefficients: the "global" coefficient that measures the association between national trends in pollution and mortality; and the "local" coefficient, derived from space by time variation, that measures the association between location-specific trends in pollution and mortality adjusted by the national trends. Absent unmeasured confounders and given valid model assumptions, the scale-specific coefficients should be similar; substantial differences in these coefficients constitute a basis for questioning the model. We derive a backfitting algorithm to fit our model to very large spatio-temporal datasets. We apply our methods to the Medicare Cohort Air Pollution Study (MCAPS), which includes individual-level information on time of death and age on a population of 18.2 million for the period 2000-2006. Results based on the global coefficient indicate a large increase in the national life expectancy for reductions in the yearly national average of PM<sub>2.5</sub>. However, this coefficient based on national trends in PM<sub>2.5</sub> and mortality is likely to be confounded by other variables trending on the national level. Confounding of the local coefficient by unmeasured factors is less likely, although it cannot be ruled out. Based on the local coefficient alone, we are not able to demonstrate any change in life expectancy for a reduction in PM<sub>2.5</sub>. We use additional survey data available for a subset of the data to investigate sensitivity of results to the inclusion of additional covariates, but both coefficients remain largely unchanged.
Biometrics · 64 Zitationen · DOI
Motivated by modern observational studies, we introduce a class of functional models that expand nested and crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in studies where the fundamental unit is a function or image. Inference is based on functional quadratics and their relationship with the underlying covariance structure of the latent processes. A computationally fast and scalable estimation procedure is developed for high-dimensional data. Methods are used in applications including high-frequency accelerometer data for daily activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity during sleep.
Electronic Journal of Statistics · 60 Zitationen · DOI
The conditional Akaike information criterion, AIC, has been frequently used for model selection in linear mixed models. We develop a general framework for the calculation of the conditional AIC for different exponential family distributions. This unified framework incorporates the conditional AIC for the Gaussian case, gives a new justification for Poisson distributed data and yields a new conditional AIC for exponentially distributed responses but cannot be applied to the binomial and gamma distributions. The proposed conditional Akaike information criteria are unbiased for finite samples, do not rely on a particular estimation method and do not assume that the variance-covariance matrix of the random effects is known. The theoretical results are investigated in a simulation study. The practical use of the method is illustrated by application to a data set on tree growth.
Journal of the American College of Cardiology · 56 Zitationen · DOI
Biostatistics · 54 Zitationen · DOI
We propose a class of estimation techniques for scalar-on-function regression where both outcomes and functional predictors may be observed at multiple visits. Our methods are motivated by a longitudinal brain diffusion tensor imaging tractography study. One of the study's primary goals is to evaluate the contemporaneous association between human function and brain imaging over time. The complexity of the study requires the development of methods that can simultaneously incorporate: (1) multiple functional (and scalar) regressors; (2) longitudinal outcome and predictor measurements per patient; (3) Gaussian or non-Gaussian outcomes; and (4) missing values within functional predictors. We propose two versions of a new method, longitudinal functional principal components regression (PCR). These methods extend the well-known functional PCR and allow for different effects of subject-specific trends in curves and of visit-specific deviations from that trend. The new methods are compared with existing approaches, and the most promising techniques are used for analyzing the tractography data.
Journal of Statistical Software · 53 Zitationen · DOI
Model selection in mixed models based on the conditional distribution is appropriate for many practical applications and has been a focus of recent statistical research. In this paper we introduce the R package cAIC4 that allows for the computation of the conditional Akaike information criterion (cAIC). Computation of the conditional AIC needs to take into account the uncertainty of the random effects variance and is therefore not straightforward. We introduce a fast and stable implementation for the calculation of the cAIC for (generalized) linear mixed models estimated with lme4 and (generalized) additive mixed models estimated with gamm4. Furthermore, cAIC4 offers a stepwise function that allows for an automated stepwise selection scheme for mixed models based on the cAIC. Examples of many possible applications are presented to illustrate the practical impact and easy handling of the package.
Statistical Modelling · 53 Zitationen · DOI
The functional linear array model (FLAM) is a unified model class for functional regression models including function-on-scalar, scalar-on-function and function-on-function regression. Mean, median, quantile as well as generalized additive regression models for functional or scalar responses are contained as special cases in this general framework. Our implementation features a broad variety of covariate effects, such as, linear, smooth and interaction effects of grouping variables, scalar and functional covariates. Computational efficiency is achieved by representing the model as a generalized linear array model. While the array structure requires a common grid for functional responses, missing values are allowed. Estimation is conducted using a boosting algorithm, which allows for numerous covariates and automatic, data-driven model selection. To illustrate the flexibility of the model class we use three applications on curing of resin for car production, heat values of fossil fuels and Canadian climate data (the last one in the electronic supplement). These require function-on-scalar, scalar-on-function and function-on-function regression models, respectively, as well as additional capabilities such as robust regression, spatial functional regression, model selection and accommodation of missings. An implementation of our methods is provided in the R add-on package FDboost .
Biometrical Journal · 50 Zitationen · DOI
Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available through a code and data supplement and on GitHub.
European Heart Journal · 50 Zitationen · DOI
The present study investigated the association of polymorphisms with inter- and intra-individual variability of C-reactive protein levels. Two minor alleles of C-reactive protein variants were associated with lower C-reactive protein concentrations. Regarding intra-individual variability, we observed associations with the minor alleles of several variants in selected candidate genes, including the CRP gene itself.
arXiv (Cornell University) · 48 Zitationen · DOI
Model selection in mixed models based on the conditional distribution is appropriate for many practical applications and has been a focus of recent statistical research. In this paper we introduce the R-package cAIC4 that allows for the computation of the conditional Akaike Information Criterion (cAIC). Computation of the conditional AIC needs to take into account the uncertainty of the random effects variance and is therefore not straightforward. We introduce a fast and stable implementation for the calculation of the cAIC for linear mixed models estimated with lme4 and additive mixed models estimated with gamm4 . Furthermore, cAIC4 offers a stepwise function that allows for a fully automated stepwise selection scheme for mixed models based on the conditional AIC. Examples of many possible applications are presented to illustrate the practical impact and easy handling of the package.
Electronic Journal of Statistics · 44 Zitationen · DOI
Regression models with functional responses and covariates constitute a powerful and increasingly important model class. However, regression with functional data poses well known and challenging problems of non-identifiability. This non-identifiability can manifest itself in arbitrarily large errors for coefficient surface estimates despite accurate predictions of the responses, thus invalidating substantial interpretations of the fitted models. We offer an accessible rephrasing of these identifiability issues in realistic applications of penalized linear function-on-function-regression and delimit the set of circumstances under which they are likely to occur in practice. Specifically, non-identifiability that persists under smoothness assumptions on the coefficient surface can occur if the functional covariate’s empirical covariance has a kernel which overlaps that of the roughness penalty of the spline estimator. Extensive simulation studies validate the theoretical insights, explore the extent of the problem and allow us to evaluate their practical consequences under varying assumptions about the data generating processes. A case study illustrates the practical significance of the problem. Based on theoretical considerations and our empirical evaluation, we provide immediately applicable diagnostics for lack of identifiability and give recommendations for avoiding estimation artifacts in practice.
The Annals of Applied Statistics · 44 Zitationen · DOI
We develop a flexible framework for modeling high-dimensional imaging data observed longitudinally. The approach decomposes the observed variability of repeatedly measured high-dimensional observations into three additive components: a subject-specific imaging random intercept that quantifies the cross-sectional variability, a subject-specific imaging slope that quantifies the dynamic irreversible deformation over multiple realizations, and a subject-visit specific imaging deviation that quantifies exchangeable effects between visits. The proposed method is very fast, scalable to studies including ultra-high dimensional data, and can easily be adapted to and executed on modest computing infrastructures. The method is applied to the longitudinal analysis of diffusion tensor imaging (DTI) data of the corpus callosum of multiple sclerosis (MS) subjects. The study includes 176 subjects observed at 466 visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 30,000 voxels.
Inhalation Toxicology · 44 Zitationen · DOI
Ambient air pollution has been associated with an increased risk of hospital admission and mortality in potentially susceptible subpopulations, including myocardial infarction (MI) survivors. The multicenter epidemiological study described in this report was set up to study the role of air pollution in eliciting inflammation in MI survivors in six European cities, Helsinki, Stockholm, Augsburg, Rome, Barcelona, and Athens. Outcomes of interest are plasma concentrations of the proinflammatory cytokine interleukin 6 (IL-6) and the acute-phase proteins C-reactive protein (CRP) and fibrinogen. In addition, the study was designed to assess the role of candidate gene polymorphisms hypothesized to lead to a modification of the short-term effects of ambient air pollution. In total, 1003 MI survivors were recruited and assessed with at least 2 repeated clinic visits without any signs of infections. In total, 5813 blood samples were collected, equivalent to an average of 5.8 repeated clinic visits per subject (97% of the scheduled 6 repeated visits). Subjects across the six cities varied with respect to risk factor profiles. Most of the subjects were nonsmokers, but light smokers were included in Rome, Barcelona, and Athens. Substantial inter- and intraindividual variability was observed for IL-6 and CRP. The study will permit assessing the role of cardiovascular disease risk factors, including ambient air pollution and genetic polymorphisms in candidate genes, in determining the inter- and the intraindividual variability in plasma IL-6, CRP, and fibrinogen concentrations in MI survivors.
Statistical Modelling · 39 Zitationen · DOI
We propose an estimation approach to analyse correlated functional data, which are observed on unequal grids or even sparsely. The model we use is a functional linear mixed model, a functional analogue of the linear mixed model. Estimation is based on dimension reduction via functional principal component analysis and on mixed model methodology. Our procedure allows the decomposition of the variability in the data as well as the estimation of mean effects of interest, and borrows strength across curves. Confidence bands for mean effects can be constructed conditionally on estimated principal components. We provide R -code implementing our approach in an online appendix. The method is motivated by and applied to data from speech production research.
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FOR 5363: KI-FOR Integration von Deep Learning und Statistik zum Verständnis strukturierter biomedizinischer Daten
university
FOR 5363: KI-FOR Integration von Deep Learning und Statistik zum Verständnis strukturierter biomedizinischer Daten
research_institute
FOR 5363: KI-FOR Integration von Deep Learning und Statistik zum Verständnis strukturierter biomedizinischer Daten
university
Statistische Modellierung unter Verwendung von Mausbewegungen zur Modellierung von Messfehlern und zur Verbesserung der Datenqualität in Web Surveys
university
FOR 5363: KI-FOR Integration von Deep Learning und Statistik zum Verständnis strukturierter biomedizinischer Daten
other
FOR 5363: KI-FOR Integration von Deep Learning und Statistik zum Verständnis strukturierter biomedizinischer Daten
university
FOR 5363: KI-FOR Integration von Deep Learning und Statistik zum Verständnis strukturierter biomedizinischer Daten
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