WorldCat Identities

Binder, Harald 1976-

Works: 73 works in 88 publications in 2 languages and 199 library holdings
Genres: Conference papers and proceedings 
Roles: Author, Other, Contributor, Editor, Publishing director
Classifications: R858.A2, 610.285
Publication Timeline
Most widely held works by Harald Binder
Flexible semi- and non-parametric modelling and prognosis for discrete outcomes by Harald Binder( Book )

3 editions published in 2006 in English and held by 13 WorldCat member libraries worldwide

DASC-PM v1.0 ein Vorgehensmodell für Data-Science-Projekte by Michael Schulz( )

2 editions published between 2020 and 2021 in German and held by 5 WorldCat member libraries worldwide

German medical data sciences, visions and bridges : proceedings of the 62nd annual meeting of the German Association of Medical Informatics, Biometry and Epidemiology (gmds e.V.) 2017 in Oldenburg (Oldenburg) - GMDS 2017 by Biometrie und Epidemiologie Deutsche Gesellschaft für Medizinische Informatik( )

5 editions published between 2017 and 2019 in English and held by 5 WorldCat member libraries worldwide

This book presents the proceedings of the 62nd annual meeting of the German Association of Medical Informatics, Biometry and Epidemiology (German Medical Data Sciences - GMDS 2017): Visions and Bridges, held in Oldenburg, Germany, in September 2017. The 242 submissions to the conference included 77 full papers, of which 42 were accepted for publication here after rigorous review. These are divided into 7 sections: teaching and training; epidemiological surveillance, screening and registration; research methods; IT infrastructure for biomedical research/data integration centers; healthcare information systems; interoperability - standards, terminologies, classification; and biomedical informatics, innovative algorithms and signal processing
Interleukin-1 promotes autoimmune neuroinflammation by suppressing endothelial heme oxygenase-1 at the blood-brain barrier by Judith Hauptmann( )

2 editions published in 2020 in English and held by 5 WorldCat member libraries worldwide

Abstract: The proinflammatory cytokine interleukin 1 (IL-1) is crucially involved in the pathogenesis of multiple sclerosis (MS) and its animal model experimental autoimmune encephalomyelitis (EAE). Herein, we studied the role of IL-1 signaling in blood-brain barrier (BBB) endothelial cells (ECs), astrocytes and microglia for EAE development, using mice with the conditional deletion of its signaling receptor IL-1R1. We found that IL-1 signaling in microglia and astrocytes is redundant for the development of EAE, whereas the IL-1R1 deletion in BBB-ECs markedly ameliorated disease severity. IL-1 signaling in BBB-ECs upregulated the expression of the adhesion molecules Vcam-1, Icam-1 and the chemokine receptor Darc, all of which have been previously shown to promote CNS-specific inflammation. In contrast, IL-1R1 signaling suppressed the expression of the stress-responsive heme catabolizing enzyme heme oxygenase-1 (HO-1) in BBB-ECs, promoting disease progression via a mechanism associated with deregulated expression of the IL-1-responsive genes Vcam1, Icam1 and Ackr1 (Darc). Mechanistically, our data emphasize a functional crosstalk of BBB-EC IL-1 signaling and HO-1, controlling the transcription of downstream proinflammatory genes promoting the pathogenesis of autoimmune neuroinflammation
A multi-cohort consortium for GEnder-Sensitive Analyses of mental health trajectories and implications for prevention (GESA) in the general population in Germany by Juliane Burghardt( )

2 editions published in 2020 in English and held by 5 WorldCat member libraries worldwide

Abstract: Introduction <br>Mental health is marked by gender differences. We formed a multi-cohort consortium to perform GEnder-Sensitive Analyses of mental health trajectories and study their implications for prevention (GESA). GESA aims at (1) identifying gender differences regarding symptoms and trajectories of mental health over the lifespan; (2) determining gender differences regarding the prevalence, impact of risk and protective factors; and (3) determining effects of mental health on primary and secondary outcomes (eg, quality of life, healthcare behaviour and utilisation).<br><br>Methods and analysis <br>We plan to perform secondary analyses on three major, ongoing, population-based, longitudinal cohorts (Gutenberg Health-Study (GHS), Study of Health in Pomerania (SHIP), Cooperative Health Research in the Augsburg Region (KORA)) with data on mental and somatic symptoms, medical assessments and diagnoses in north-east, middle and southern Germany (n>40 000). Meta-analytic techniques (using DataSHIELD framework) will be used to combine aggregated data from these cohorts. This process will inform about heterogeneity of effects. Longitudinal regression models will estimate sex-specific trajectories and effects of risk and protective factors and secondary outcomes.<br><br>Ethics and dissemination <br>The cohorts were approved by the ethics committees of the Statutory Physician Board of Rhineland-Palatinate (837.020.07; GHS), the University of Greifswald (BB 39/08; SHIP) and the Bavarian Chamber of Physicians (06068; KORA). Together with stakeholders in medical care and medical training, findings will be translated and disseminated into gender-sensitive health promotion and prevention
App-controlled treatment monitoring and support for head and neck cancer patients (APCOT): protocol for a prospective randomized controlled trial by Tetyana Sprave( )

1 edition published in 2020 in English and held by 3 WorldCat member libraries worldwide

Abstract: Background: Head and neck cancers (HNCs) are among the most common malignancies, which often require multimodal treatment that includes radiation therapy and chemotherapy. Patients with HNC have a high burden of symptoms due to both the damaging effects of the tumor and the aggressive multimodal treatment. Close symptom monitoring over the course of the disease may help to identify patients in need of medical interventions.<br><br>Objective: This APCOT (App-Controlled Treatment Monitoring and Support for Head and Neck Cancer Patients) trial is designed to assess the feasibility of monitoring HNC patients during the course of (chemo)radiation therapy daily using a mobile app. Additionally, symptom patterns, patient satisfaction, and quality of life will be measured in app-monitored patients in comparison to a patient cohort receiving standard-of-care physician appointments, and health economy aspects of app monitoring will be analyzed.<br><br>Methods: This prospective randomized single-center trial will evaluate the feasibility of integrating electronic patient-reported outcome measures (ePROMs) into the treatment workflow of HNC patients. Patients undergoing definitive or adjuvant (chemo)radiation therapy as part of their HNC treatment at the Department of Radiation Oncology, University Medical Center Freiburg (Freiburg, Germany) will receive weekly physician appointments and additional appointments as requested to monitor and potentially treat symptoms during the course of treatment. Patients in the experimental arm will additionally be monitored daily using a dedicated app regarding their disease- and treatment-related symptoms, quality of life, and need for personal physician appointments. The feasibility of ePROM monitoring will be tested as the primary endpoint and will be defined if ≥80% of enrolled patients have answered ≥80% of their daily app-based questions. Quality of life will be assessed using the validated European Organisation for Research and Treatment of Cancer questionnaires, and patient satisfaction will be measured by the validated Patient Satisfaction Questionnaire Short Form at the initiation, in the middle, and at completion of radiation therapy, as well as at follow-up examinations. Additionally, the number and duration of physician appointments during the course of radiation therapy will be quantified for both ePROM-monitored and standard-of-care patients.<br><br>Results: This trial will enroll 100 patients who will be randomized (1:1) between the experimental arm with ePROM monitoring and the control arm with standard patient care. Recruitment will take 18 months, and trial completion is planned at 24 months after enrollment of the last patient.<br><br>Conclusions: This trial will establish the feasibility of close ePROM monitoring of HNC patients undergoing (chemo)radiation therapy. The results can form the basis for further trials investigating potential clinical benefits of detailed symptom monitoring and patient-centered care in HNC patients regarding oncologic outcomes and quality of life.<br><br>Trial Registration: German Clinical Trials Register DRKS00020491;
Genetic determinants of ototoxicity during and after childhood cancer treatment: protocol for the PanCareLIFE study by Eva Clemens( )

1 edition published in 2019 in English and held by 3 WorldCat member libraries worldwide

Similarities and differences of mental health in women and men: a systematic review of findings in three large German cohorts by Danielle Otten( )

1 edition published in 2021 in English and held by 3 WorldCat member libraries worldwide

Abstract: In Germany, large, population-based cohort studies have been implemented in order to identify risk and protective factors for maintaining health across the life span. The purpose of this systematic review is to analyse findings from three large ongoing cohorts and to identify sex-specific prevalence rates, risk and protective factors for mental health. Published studies from the Cooperative Health Research in the Region Augsburg (KORA), the Study of Health in Pomerania (SHIP) and the Gutenberg Health Study (GHS)), representing the southern, north-eastern and middle parts of Germany, were identified through searches of the databases PubMed and Web of Science. A total of 52 articles was identified from the start of each cohort until June 2019. Articles reporting prevalence rates of mental health [N = 22], explanatory factors for mental health [N = 25], or both [N = 5] were identified. Consistent across cohorts, higher prevalence rates of internalizing disorders were found for women and more externalizing disorders for men. Risk and protective factors for mental health included social factors, lifestyle, physical health, body mass index (BMI), diabetes, genetic and biological factors. In all areas, differences and similarities were found between women and men. The most evident were the sex-specific risk profiles for depression with mostly external risk factors for men and internal risk factors for women. Gender was not assessed directly, therefore we examined whether socioeconomic and family-related factors reflecting gender roles or institutionalized gender could be used as a proxy for gender. Overall, this systematic review shows differences and similarities in prevalence rates and determinants of mental health indicators between women and men. They underline the importance of focussing on sex specific approaches in mental health research and in the development of prevention measures. Current research on mental health still lacks focus on gender aspects. Therefore, an increased focus on sex and gender in mental health research is of great importance
Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach by Jan Wolff( )

1 edition published in 2020 in English and held by 3 WorldCat member libraries worldwide

Abstract: Background<br>A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier.<br><br>Methods<br>The study included consecutively discharged patients between 1st of January 2017 and 31st of December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals.<br><br>Results<br>The study included 45,388 inpatient episodes. The models' performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping.<br><br>Conclusion<br>The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients
ideal: an R/Bioconductor package for interactive differential expression analysis by Federico Marini( )

1 edition published in 2020 in English and held by 3 WorldCat member libraries worldwide

Abstract: Background<br>RNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking.<br><br>Results<br>We developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility.<br><br>Conclusion<br>ideal is distributed as an R package in the Bioconductor project (, and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand
Exploring generative deep learning for omics data using log-linear models by Moritz Hess( )

1 edition published in 2020 in English and held by 3 WorldCat member libraries worldwide

Abstract: Motivation<br>Following many successful applications to image data, deep learning is now also increasingly considered for omics data. In particular, generative deep learning not only provides competitive prediction performance, but also allows for uncovering structure by generating synthetic samples. However, exploration and visualization is not as straightforward as with image applications.<br><br>Results<br>We demonstrate how log-linear models, fitted to the generated, synthetic data can be used to extract patterns from omics data, learned by deep generative techniques. Specifically, interactions between latent representations learned by the approaches and generated synthetic data are used to determine sets of joint patterns. Distances of patterns with respect to the distribution of latent representations are then visualized in low-dimensional coordinate systems, e.g. for monitoring training progress. This is illustrated with simulated data and subsequently with cortical single-cell gene expression data. Using different kinds of deep generative techniques, specifically variational autoencoders and deep Boltzmann machines, the proposed approach highlights how the techniques uncover underlying structure. It facilitates the real-world use of such generative deep learning techniques to gain biological insights from omics data.<br><br>Availability and implementation<br>The code for the approach as well as an accompanying Jupyter notebook, which illustrates the application of our approach, is available via the GitHub repository:
Statistical computing 2009 - Abstracts der 41. Jahrestagung( )

1 edition published in 2009 in English and held by 3 WorldCat member libraries worldwide

SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial by Pamela Franco( )

1 edition published in 2020 in English and held by 3 WorldCat member libraries worldwide

Abstract: Background<br>The revised 2016 WHO-Classification of CNS-tumours now integrates molecular information of glial brain tumours for accurate diagnosis as well as for the development of targeted therapies. In this prospective study, our aim is to investigate the predictive value of MR-spectroscopy in order to establish a solid preoperative molecular stratification algorithm of these tumours. We will process a 1H MR-spectroscopy sequence within a radiomics analytics pipeline.<br><br>Methods<br>Patients treated at our institution with WHO-Grade II, III and IV gliomas will receive preoperative anatomical (T2- and T1-weighted imaging with and without contrast enhancement) and proton MR spectroscopy (MRS) by using chemical shift imaging (MRS) (5 × 5 × 15 mm3 voxel size). Tumour regions will be segmented and co-registered to corresponding spectroscopic voxels. Raw signals will be processed by a deep-learning approach for identifying patterns in metabolic data that provides information with respect to the histological diagnosis as well patient characteristics obtained and genomic data such as target sequencing and transcriptional data.<br><br>Discussion<br>By imaging the metabolic profile of a glioma using a customized chemical shift 1H MR spectroscopy sequence and by processing the metabolic profiles with a machine learning tool we intend to non-invasively uncover the genetic signature of gliomas. This work-up will support surgical and oncological decisions to improve personalized tumour treatment.<br><br>Trial registration<br>This study was initially registered under another name and was later retrospectively registered under the current name at the German Clinical Trials Register (DRKS) under DRKS00019855
Dealing with prognostic signature instability: a strategy illustrated for cardiovascular events in patients with end-stage renal disease by Harald Binder( )

2 editions published in 2016 in English and held by 3 WorldCat member libraries worldwide

The impact of age on the association between physical activity and white matter integrity in cognitively healthy older adults by Dominik Wolf( )

1 edition published in 2020 in English and held by 3 WorldCat member libraries worldwide

Abstract: Cognition emerges from coordinated processing among distributed cortical brain regions, enabled through interconnected white matter networks. Cortical disconnection caused by age-related decline in white matter integrity (WMI) is likely to contribute to age-related cognitive decline. Physical activity (PA) has been suggested to have beneficial effects on white matter structure. However, its potential to counteract age-related decline in WMI is not yet well established. The present explorative study analyzed if PA was associated with WMI in cognitively healthy older adults and if this association was modulated by age. Forty-four cognitively healthy older individuals (aged 60-88 years) with diffusion-tensor imaging (DTI) and PA measurements were included from the AgeGain study. Voxelwise analysis using Tract-Based Spatial Statistics (TBSS) demonstrated that PA was associated with WMI in older adults. However, results emphasized that this association was restricted to high age. The association between PA and WMI was found in widespread white matter regions suggesting a global rather than a regional effect. Supplementary analyses demonstrated an association between the integrity of these regions and the performance in memory [verbal learning and memory test (VLMT)] and executive functioning (Tower of London).Results of the present explorative study support the assumption that PA is associated with WMI in older adults. However, results emphasize that this association is restricted to high age. Since cognitive decline in the elderly is typically most pronounced in later stages of aging, PA qualifies as a promising tool to foster resilience against age-related cognitive decline, via the preservation of the integrity of the brains WM
Dorsolateral prefrontal functional connectivity predicts working memory training gains by Sofia Faraza( )

1 edition published in 2021 in English and held by 3 WorldCat member libraries worldwide

Abstract: Background: Normal aging is associated with working memory decline. A decrease in working memory performance is associated with age-related changes in functional activation patterns in the dorsolateral prefrontal cortex (DLPFC). Cognitive training can improve cognitive performance in healthy older adults. We implemented a cognitive training study to assess determinants of generalization of training gains to untrained tasks, a key indicator for the effectiveness of cognitive training. We aimed to investigate the association of resting-state functional connectivity (FC) of DLPFC with working memory performance improvement and cognitive gains after the training.<br><br>Method: A sample of 60 healthy older adults (mean age: 68 years) underwent a 4-week neuropsychological training, entailing a working memory task. Baseline resting-state functional MRI (rs-fMRI) images were acquired in order to investigate the FC of DLPFC. To evaluate training effects, participants underwent a neuropsychological assessment before and after the training. A second follow-up assessment was applied 12 weeks after the training. We used cognitive scores of digit span backward and visual block span backward tasks representing working memory function. The training group was divided into subjects who had and who did not have training gains, which was defined as a higher improvement in working memory tasks than the control group (N = 19).<br><br>Results: A high FC of DLPFC of the right hemisphere was significantly associated with training gains and performance improvement in the visuospatial task. The maintenance of cognitive gains was restricted to the time period directly after the training. The training group showed performance improvement in the digit span backward task.<br><br>Conclusion: Functional activation patterns of the DLPFC were associated with the degree of working memory training gains and visuospatial performance improvement. Although improvement through cognitive training and acquisition of training gains are possible in aging, they remain limited
Recovery of original individual person data (IPD) inferences from empirical IPD summaries only: applications to distributed computing under disclosure constraints by Federico Bonofiglio( )

1 edition published in 2020 in English and held by 3 WorldCat member libraries worldwide

Abstract: There are many settings where individual person data (IPD) are not available, due to privacy or technical reasons, and one must work with IPD proxies, such as summary statistics, to approximate original IPD inferences, that is, the results of statistical analyses that would ideally have been performed on individual-level data. For instance, in a distributed computing setting, as implemented in the DataSHIELD software framework, different centers can only share IPD proxies to obtain pooled IPD inferences. Such privacy requirements limit the scope of statistical investigation. For example, it can be challenging to perform between-center random-effect regression models. To increase modeling freedom we propose a method that only uses simple nondisclosive summaries of the original IPD as input, such as empirical marginal moments and correlation matrices, and generates artificial data compatible with those summary features. Specifically, data are generated from a Gaussian copula with marginal and joint components specified by the above summaries. The goal is to reproduce original IPD features in the artificial data, such that original IPD inferences are recovered from the artificial data. In an application example, and through simulations, we show that we can recover estimates of a multivariable IPD random-effect logistic regression, from artificial data generated via the Gaussian copula using the above IPD summaries, suggesting the proposed approach provides a generally applicable strategy for distributed computing settings with data protection constraints
State of the art in selection of variables and functional forms in multivariable analysis - outstanding issues by Willi Sauerbrei( )

1 edition published in 2020 in English and held by 3 WorldCat member libraries worldwide

Abstract: Background<br>How to select variables and identify functional forms for continuous variables is a key concern when creating a multivariable model. Ad hoc 'traditional' approaches to variable selection have been in use for at least 50 years. Similarly, methods for determining functional forms for continuous variables were first suggested many years ago. More recently, many alternative approaches to address these two challenges have been proposed, but knowledge of their properties and meaningful comparisons between them are scarce. To define a state of the art and to provide evidence-supported guidance to researchers who have only a basic level of statistical knowledge, many outstanding issues in multivariable modelling remain. Our main aims are to identify and illustrate such gaps in the literature and present them at a moderate technical level to the wide community of practitioners, researchers and students of statistics.<br><br>Methods<br>We briefly discuss general issues in building descriptive regression models, strategies for variable selection, different ways of choosing functional forms for continuous variables and methods for combining the selection of variables and functions. We discuss two examples, taken from the medical literature, to illustrate problems in the practice of modelling.<br><br>Results<br>Our overview revealed that there is not yet enough evidence on which to base recommendations for the selection of variables and functional forms in multivariable analysis. Such evidence may come from comparisons between alternative methods. In particular, we highlight seven important topics that require further investigation and make suggestions for the direction of further research.<br><br>Conclusions<br>Selection of variables and of functional forms are important topics in multivariable analysis. To define a state of the art and to provide evidence-supported guidance to researchers who have only a basic level of statistical knowledge, further comparative research is required
Thrombin generation in cardiovascular disease and mortality - results from the Gutenberg Health Study by Pauline C. S. van Paridon( )

1 edition published in 2020 in English and held by 3 WorldCat member libraries worldwide

Abstract: Thrombin generation may be a potential tool to improve risk stratification for cardiovascular diseases. This study aims to explore the relation between thrombin generation and cardiovascular risk factors, cardiovascular diseases, and total mortality. For this study, N=5000 subjects from the population-based Gutenberg Health Study were analysed in a highly standardized setting. Thrombin generation was assessed by the Calibrated Automated Thrombogram method at 1 and 5 pM tissue factors trigger in platelet poor plasma. Lag time, endogenous thrombin potential, and peak height were derived from the thrombin generation curve. Sex-specific multivariable linear regression analysis adjusted for age, cardiovascular risk factors, cardiovascular diseases and therapy, was used to assess clinical determinants of thrombin generation. Cox regression models adjusted for age, sex, cardiovascular risk factors and vitamin K antagonists investigated the association between thrombin generation parameters and total mortality. Lag time was positively associated with obesity and dyslipidaemia for both sexes (p<0.0001). Obesity was also positively associated with endogenous thrombin potential in both sexes (p<0.0001) and peak height in males (1 pM tissue factor, p=0.0048) and females (p<0.0001). Cox regression models showed an increased mortality in individuals with lag time (1 pM tissue factor, hazard ratio=1.46, [95% CI: 1.07; 2.00], p=0.018) and endogenous thrombin potential (5 pM tissue factor, hazard ratio = 1.50, [1.06; 2.13], p=0.023) above the 95th percentile of the reference group, independent of the cardiovascular risk profile. This large-scale study demonstrates traditional cardiovascular risk factors, particularly obesity, as relevant determinants of thrombin generation. Lag time and endogenous thrombin potential were found as potentially relevant predictors of increased total mortality, which deserves further investigation
Identifying prognostic SNPs in clinical cohorts: complementing univariate analyses by resampling and multivariable modeling by Stefanie Hieke-Schulz( )

1 edition published in 2016 in English and held by 3 WorldCat member libraries worldwide

Abstract: Clinical cohorts with time-to-event endpoints are increasingly characterized by measurements of a number of single nucleotide polymorphisms that is by a magnitude larger than the number of measurements typically considered at the gene level. At the same time, the size of clinical cohorts often is still limited, calling for novel analysis strategies for identifying potentially prognostic SNPs that can help to better characterize disease processes. We propose such a strategy, drawing on univariate testing ideas from epidemiological case-controls studies on the one hand, and multivariable regression techniques as developed for gene expression data on the other hand. In particular, we focus on stable selection of a small set of SNPs and corresponding genes for subsequent validation. For univariate analysis, a permutation-based approach is proposed to test at the gene level. We use regularized multivariable regression models for considering all SNPs simultaneously and selecting a small set of potentially important prognostic SNPs. Stability is judged according to resampling inclusion frequencies for both the univariate and the multivariable approach. The overall strategy is illustrated with data from a cohort of acute myeloid leukemia patients and explored in a simulation study. The multivariable approach is seen to automatically focus on a smaller set of SNPs compared to the univariate approach, roughly in line with blocks of correlated SNPs. This more targeted extraction of SNPs results in more stable selection at the SNP as well as at the gene level. Thus, the multivariable regression approach with resampling provides a perspective in the proposed analysis strategy for SNP data in clinical cohorts highlighting what can be added by regularized regression techniques compared to univariate analyses
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Flexible semi- and non-parametric modelling and prognosis for discrete outcomes
German medical data sciences, visions and bridges : proceedings of the 62nd annual meeting of the German Association of Medical Informatics, Biometry and Epidemiology (gmds e.V.) 2017 in Oldenburg (Oldenburg) - GMDS 2017
Alternative Names
Harald Binder wetenschapper

English (28)

German (2)