Manitz, Juliane

[["dc.bibliographiccitation.firstpage","6742763"],["dc.bibliographiccitation.journal","Computational and mathematical methods in medicine"],["dc.bibliographiccitation.lastpage","17"],["dc.bibliographiccitation.volume","2017"],["dc.contributor.author","Friedrichs, Stefanie"],["dc.contributor.author","Manitz, Juliane"],["dc.contributor.author","Burger, Patricia"],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Chang-Claude, Jenny"],["dc.contributor.author","Wichmann, Heinz-Erich"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Bickeböller, Heike"],["dc.contributor.author","Hofner, Benjamin"],["dc.date.accessioned","2018-03-13T14:55:43Z"],["dc.date.available","2018-03-13T14:55:43Z"],["dc.date.issued","2017"],["dc.description.abstract","The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility."],["dc.identifier.doi","10.1155/2017/6742763"],["dc.identifier.pmid","28785300"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14774"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/13019"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.relation.eissn","1748-6718"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","64"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Human Heredity"],["dc.bibliographiccitation.lastpage","75"],["dc.bibliographiccitation.volume","76"],["dc.contributor.author","Freytag, Saskia"],["dc.contributor.author","Manitz, Juliane"],["dc.contributor.author","Schlather, Martin"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Chang-Claude, Jenny"],["dc.contributor.author","Heinrich, Joachim"],["dc.contributor.author","Bickeböller, Heike"],["dc.date.accessioned","2017-09-07T11:47:18Z"],["dc.date.available","2017-09-07T11:47:18Z"],["dc.date.issued","2014"],["dc.description.abstract","Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms."],["dc.identifier.doi","10.1159/000357567"],["dc.identifier.gro","3149315"],["dc.identifier.pmid","24434848"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10822"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5978"],["dc.language.iso","en"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","S. Karger AG"],["dc.relation.eissn","1423-0062"],["dc.relation.issn","0001-5652"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0164508"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Tahden, Maike"],["dc.contributor.author","Manitz, Juliane"],["dc.contributor.author","Baumgardt, Klaus"],["dc.contributor.author","Fell, Gerhard"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Hegasy, Guido"],["dc.date.accessioned","2017-09-07T11:47:15Z"],["dc.date.available","2017-09-07T11:47:15Z"],["dc.date.issued","2016"],["dc.description.abstract","In 2011, a large outbreak of entero-hemorrhagic E. coli (EHEC) and hemolytic uremic syndrome (HUS) occurred in Germany. The City of Hamburg was the first focus of the epidemic and had the highest incidences among all 16 Federal States of Germany. In this article, we present epidemiological characteristics of the Hamburg notification data. Evaluating the epicurves retrospectively, we found that the first epidemiological signal of the outbreak, which was in form of a HUS case cluster, was received by local health authorities when already 99 EHEC and 48 HUS patients had experienced their first symptoms. However, only two EHEC and seven HUS patients had been notified. Middle-aged women had the highest risk for contracting the infection in Hamburg. Furthermore, we studied timeliness of case notification in the course of the outbreak. To analyze the spatial distribution of EHEC/HUS incidences in 100 districts of Hamburg, we mapped cases' residential addresses using geographic information software. We then conducted an ecological study in order to find a statistical model identifying associations between local socio-economic factors and EHEC/HUS incidences in the epidemic. We employed a Bayesian Poisson model with covariates characterizing the Hamburg districts as well as incorporating structured and unstructured spatial effects. The Deviance Information Criterion was used for stepwise variable selection. We applied different modeling approaches by using primary data, transformed data, and preselected subsets of transformed data in order to identify socio-economic factors characterizing districts where EHEC/HUS outbreak cases had their residence."],["dc.identifier.doi","10.1371/journal.pone.0164508"],["dc.identifier.gro","3149308"],["dc.identifier.pmid","27723830"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13758"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5970"],["dc.language.iso","en"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","PLoS Currents"],["dc.contributor.author","Manitz, Juliane"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Schlather, Martin"],["dc.contributor.author","Helbing, Dirk"],["dc.contributor.author","Brockmann, Dirk"],["dc.date.accessioned","2017-09-07T11:47:13Z"],["dc.date.available","2017-09-07T11:47:13Z"],["dc.date.issued","2014"],["dc.description.abstract","The key challenge during food-borne disease outbreaks, e.g. the 2011 EHEC/HUS outbreak in Germany, is the design of efficient mitigation strategies based on a timely identification of the outbreak’s spatial origin. Standard public health procedures typically use case-control studies and tracings along food shipping chains. These methods are time-consuming and suffer from biased data collected slowly in patient interviews. Here we apply a recently developed, network-theoretical method to identify the spatial origin of food-borne disease outbreaks. Thereby, the network captures the transportation routes of contaminated foods. The technique only requires spatial information on case reports regularly collected by public health institutions and a model for the underlying food distribution network. The approach is based on the idea of replacing the conventional geographic distance with an effective distance that is derived from the topological structure of the underlying food distribution network. We show that this approach can efficiently identify most probable epicenters of food-borne disease outbreaks. We assess and discuss the method in the context of the 2011 EHEC epidemic. Based on plausible assumptions on the structure of the national food distribution network, the approach can correctly localize the origin of the 2011 German EHEC/HUS outbreak."],["dc.identifier.doi","10.1371/currents.outbreaks.f3fdeb08c5b9de7c09ed9cbcef5f01f2"],["dc.identifier.gro","3149301"],["dc.identifier.pmid","24818065"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5962"],["dc.language.iso","en"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","2157-3999"],["dc.title","Origin Detection During Food-borne Disease Outbreaks - A Case Study of the 2011 EHEC/HUS Outbreak in Germany"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","521"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Journal of the Royal Statistical Society. Series C, Applied statistics"],["dc.bibliographiccitation.lastpage","536"],["dc.bibliographiccitation.volume","66"],["dc.contributor.author","Manitz, Juliane"],["dc.contributor.author","Harbering, Jonas"],["dc.contributor.author","Schmidt, Marie"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Schoebel, Anita"],["dc.date.accessioned","2017-09-07T11:47:46Z"],["dc.date.available","2017-09-07T11:47:46Z"],["dc.date.issued","2016"],["dc.identifier.doi","10.1111/rssc.12176"],["dc.identifier.gro","3149364"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6033"],["dc.language.iso","en"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","0035-9254"],["dc.title","Source estimation for propagation processes on complex networks with an application to delays in public transportation systems"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]