Wingender, Edgar

[["dc.bibliographiccitation.firstpage","1190"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","1197"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Konovalova, T."],["dc.contributor.author","Waleev, T."],["dc.contributor.author","Cheremushkin, E."],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T09:48:57Z"],["dc.date.available","2018-11-07T09:48:57Z"],["dc.date.issued","2006"],["dc.description.abstract","Motivation: Functionally related genes involved in the same molecular-genetic, biochemical or physiological process are often regulated coordinately. Such regulation is provided by precisely organized binding of a multiplicity of special proteins [transcription factors (TFs)] to their target sites (cis-elements) in regulatory regions of genes. Cis-element combinations provide a structural basis for the generation of unique patterns of gene expression. Results: Here we present a new approach for defining promoter models based on the composition of TF binding sites and their pairs. We utilize a multicomponent fitness function for selection of the promoter model that fits best to the observed gene expression profile. We demonstrate examples of successful application of the fitness function with the help of a genetic algorithm for the analysis of functionally related or co-expressed genes as well as testing on simulated and permutated data."],["dc.identifier.doi","10.1093/bioinformatics/btl041"],["dc.identifier.isi","000237319300007"],["dc.identifier.pmid","16473870"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35413"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","1367-4803"],["dc.title","Composite Module Analyst: a fitness-based tool for identification of transcription factor binding site combinations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","European Journal of Cell Biology"],["dc.bibliographiccitation.volume","84"],["dc.contributor.author","Choi, C."],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Krull, M."],["dc.contributor.author","Pistor, S."],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T11:19:06Z"],["dc.date.available","2018-11-07T11:19:06Z"],["dc.date.issued","2005"],["dc.format.extent","23"],["dc.identifier.isi","000228527700027"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/55192"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Urban & Fischer Verlag"],["dc.publisher.place","Jena"],["dc.relation.conference","Annual Meeting of the Deutsche-Gesellschaft-fur-Zellbiologie"],["dc.relation.eventlocation","Heidelberg, GERMANY"],["dc.relation.issn","0171-9335"],["dc.title","The TRANSPATH((R)) database: Identifying candidate pathways regulating the ubiquitin ligase parkin, a marker of Parkinson disease"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","481"],["dc.bibliographiccitation.issue","5-6"],["dc.bibliographiccitation.journal","SAR and QSAR in Environmental Research"],["dc.bibliographiccitation.lastpage","494"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Valeev, T."],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T11:20:44Z"],["dc.date.available","2018-11-07T11:20:44Z"],["dc.date.issued","2008"],["dc.description.abstract","Different signal transduction pathways leading to the activation of transcription factors (TFs) converge at key molecules that master the regulation of many cellular processes. Such crossroads of signalling networks often appear as Achilles Heels causing a disease when not functioning properly. Novel computational tools are needed for analysis of the gene expression data in the context of signal transduction and gene regulatory pathways and for identification of the key nodes in the networks. An integrated computational system, ExPlain (TM) (www.biobase.de) was developed for causal interpretation of gene expression data and identification of key signalling molecules. The system utilizes data from two databases (TRANSFAC (R) and TRANSPATH (R)) and integrates two programs: (1) Composite Module Analyst (CMA) analyses 5'-upstream regions of co-expressed genes and applies a genetic algorithm to reveal composite modules (CMs) consisting of co-occurring single TF binding sites and composite elements; (2) ArrayAnalyzer (TM) is a fast network search engine that analyses signal transduction networks controlling the activities of the corresponding TFs and seeks key molecules responsible for the observed concerted gene activation. ExPlain (TM) system was applied to microarray data on inflammatory bowel diseases (IBD). The results obtained suggest a number of highly interesting biological hypotheses about molecular mechanisms of pathological genetic disregulation."],["dc.identifier.doi","10.1080/10629360802083806"],["dc.identifier.isi","000259995500004"],["dc.identifier.pmid","18853298"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/55611"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Taylor & Francis Ltd"],["dc.relation.issn","1062-936X"],["dc.title","ExPlain (TM): finding upstream drug targets in disease gene regulatory networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","EuPA open proteomics"],["dc.bibliographiccitation.lastpage","13"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Valeev, Tagir"],["dc.contributor.author","Koschmann, Jeannette"],["dc.contributor.author","Poroikov, Vladimir"],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2019-07-26T14:05:50Z"],["dc.date.available","2019-07-26T14:05:50Z"],["dc.date.issued","2016-12"],["dc.description.abstract","We present an \"upstream analysis\" strategy for causal analysis of multiple \"-omics\" data. It analyzes promoters using the TRANSFAC database, combines it with an analysis of the upstream signal transduction pathways and identifies master regulators as potential drug targets for a pathological process. We applied this approach to a complex multi-omics data set that contains transcriptomics, proteomics and epigenomics data. We identified the following potential drug targets against induced resistance of cancer cells towards chemotherapy by methotrexate (MTX): TGFalpha, IGFBP7, alpha9-integrin, and the following chemical compounds: zardaverine and divalproex as well as human metabolites such as nicotinamide N-oxide."],["dc.identifier.doi","10.1016/j.euprot.2016.09.002"],["dc.identifier.pmid","29900117"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62103"],["dc.language.iso","en"],["dc.relation.eissn","2212-9685"],["dc.relation.issn","2212-9685"],["dc.title","Multi-omics \"upstream analysis\" of regulatory genomic regions helps identifying targets against methotrexate resistance of colon cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","412"],["dc.bibliographiccitation.issue","18"],["dc.bibliographiccitation.journal","The Journal of Open Source Software"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2019-07-26T14:03:43Z"],["dc.date.available","2019-07-26T14:03:43Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.21105/joss.00412"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62102"],["dc.language.iso","en"],["dc.relation.issn","2475-9066"],["dc.title","geneXplainR: An R interface for the geneXplain platform"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1512"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","1516"],["dc.bibliographiccitation.volume","20"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Tikunov, Y."],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T10:47:29Z"],["dc.date.available","2018-11-07T10:47:29Z"],["dc.date.issued","2004"],["dc.description.abstract","Motivation: Transcription factor binding sites often differ significantly in their primary sequence and can hardly be aligned. Often one set of sites can contain several subsets of sequences that follow not just one but several different patterns. There is a need for sensitive methods to reveal multiple patterns in unaligned sets of sequences. Results: We developed a novel method for analysis of unaligned sets of sequences based on kernel estimation. The method is able to reveal 'multiple local patterns'-a set of weight matrices. Every weight matrix characterizes a pattern that can be found in a significant subset of sequences under analysis. The method developed has been compared with several other methods of pattern discovery such as Gibbs sampling, MEME, CONSENSUS, MULTIPROFILER and PROJECTION. The kernel method showed the best performance in terms of how close the revealed weight matrices are to the original ones. We applied the kernel method to analyze three samples of promoters (cell-cycle, T-cells and muscle-specific). We compared the multiple patterns revealed with the TRANSFAC library of weight matrices and found a strong similarity to several weight matrices for transcription factors known to be involved in the mentioned specific gene regulation."],["dc.identifier.doi","10.1093/bioinformatics/bth111"],["dc.identifier.isi","000222402400008"],["dc.identifier.pmid","15231544"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/47975"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.publisher.place","Oxford"],["dc.relation.conference","18th German Conference on Bioinformatics"],["dc.relation.eventlocation","Tech Univ Munchen, Garching, GERMANY"],["dc.relation.issn","1367-4803"],["dc.title","Recognition of multiple patterns in unaligned sets of sequences: comparison of kernel clustering method with other methods"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","D104"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","D107"],["dc.bibliographiccitation.volume","34"],["dc.contributor.author","Chen, Xin"],["dc.contributor.author","Wu, J."],["dc.contributor.author","Hornischer, Klaus"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T10:39:36Z"],["dc.date.available","2018-11-07T10:39:36Z"],["dc.date.issued","2006"],["dc.description.abstract","TiProD is a database of human promoter sequences for which some functional features are known. It allows a user to query individual promoters and the expression pattern they mediate, gene expression signatures of individual tissues, and to retrieve sets of promoters according to their tissue-specific activity or according to individual Gene Ontology terms the corresponding genes are assigned to. We have defined a measure for tissue-specificity that allows the user to discriminate between ubiquitously and specifically expressed genes. The database is accessible at http://tiprod.cbi.pku.edu.cn:8080/index.html."],["dc.identifier.doi","10.1093/nar/gkj113"],["dc.identifier.isi","000239307700022"],["dc.identifier.pmid","16381824"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/46090"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","0305-1048"],["dc.title","TiProD: the Tissue-specific Promoter Database"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","D546"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","D551"],["dc.bibliographiccitation.volume","34"],["dc.contributor.author","Krull, Mathias"],["dc.contributor.author","Pistor, Susanne"],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Reuter, Ingmar"],["dc.contributor.author","Kronenberg, Deborah"],["dc.contributor.author","Michael, Holger"],["dc.contributor.author","Schwarzer, Knut"],["dc.contributor.author","Potapov, Anatolij P."],["dc.contributor.author","Choi, Claudia"],["dc.contributor.author","Kel-Margoulis, Olga"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T10:39:39Z"],["dc.date.available","2018-11-07T10:39:39Z"],["dc.date.issued","2006"],["dc.description.abstract","TRANSPATH (R) is a database about signal transduction events. It provides information about signaling molecules, their reactions and the pathways these reactions constitute. The representation of signaling molecules is organized in a number of orthogonal hierarchies reflecting the classification of the molecules, their species-specific or generic features, and their post-translational modifications. Reactions are similarly hierarchically organized in a three-layer architecture, differentiating between reactions that are evidenced by individual publications, generalizations of these reactions to construct species-independent 'reference pathways' and the 'semantic projections' of these pathways. A number of search and browse options allow easy access to the database contents, which can be visualized with the tool PathwayBuilder (TM). The module PathoSign adds data about pathologically relevant mutations in signaling components, including their genotypes and phenotypes. TRANSPATH (R) and PathoSign can be used as encyclopaedia, in the educational process, for vizualization and modeling of signal transduction networks and for the analysis of gene expression data. TRANSPATH (R) Public 6.0 is freely accessible for users from non-profit organizations under http://www.gene-regulation.com/pub/databases.html."],["dc.identifier.doi","10.1093/nar/gkj107"],["dc.identifier.isi","000239307700118"],["dc.identifier.pmid","16381929"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/46101"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","1362-4962"],["dc.relation.issn","0305-1048"],["dc.title","TRANSPATH (R): an information resource for storing and visualizing signaling pathways and their pathological aberrations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","518"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Briefings in Bioinformatics"],["dc.bibliographiccitation.lastpage","531"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Michael, Holger"],["dc.contributor.author","Hogan, Jennifer D."],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Kel-Margoulis, Olga"],["dc.contributor.author","Schacherer, Frank"],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T11:09:20Z"],["dc.date.available","2018-11-07T11:09:20Z"],["dc.date.issued","2008"],["dc.description.abstract","Translating the exponentially growing amount of omics data into knowledge usable for a personalized medicine approach poses a formidable challenge. In this articletaking diabetes as a use casewe present strategies for developing data repositories into computer-accessible knowledge sources that can be used for a systemic view on the molecular causes of diseases, thus laying the foundation for systems pathology."],["dc.identifier.doi","10.1093/bib/bbn038"],["dc.identifier.isi","000261996300007"],["dc.identifier.pmid","19073714"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/52985"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","1467-5463"],["dc.title","Building a knowledge base for systems pathology"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]