Wingender, Edgar

[["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","270"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Microarrays"],["dc.bibliographiccitation.lastpage","286"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Koschmann, Jeannette"],["dc.contributor.author","Bhar, Anirban"],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Kel, Alexander"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2019-07-09T11:41:23Z"],["dc.date.available","2019-07-09T11:41:23Z"],["dc.date.issued","2015"],["dc.description.abstract","A strategy is presented that allows a causal analysis of co-expressed genes, which may be subject to common regulatory influences. A state-of-the-art promoter analysis for potential transcription factor (TF) binding sites in combination with a knowledge-based analysis of the upstream pathway that control the activity of these TFs is shown to lead to hypothetical master regulators. This strategy was implemented as a workflow in a comprehensive bioinformatic software platform. We applied this workflow to gene sets that were identified by a novel triclustering algorithm in naphthalene-induced gene expression signatures of murine liver and lung tissue. As a result, tissue-specific master regulators were identified that are known to be linked with tumorigenic and apoptotic processes. To our knowledge, this is the first time that genes of expression triclusters were used to identify upstream regulators."],["dc.identifier.doi","10.3390/microarrays4020270"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12010"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58417"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/258236/EU//SYSCOL"],["dc.relation.euproject","SYSCOL"],["dc.relation.issn","2076-3905"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","“Upstream Analysis”: An Integrated Promoter-Pathway Analysis Approach to Causal Interpretation of Microarray Data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","S4"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","20"],["dc.contributor.author","Kel, Alexander"],["dc.contributor.author","Boyarskikh, Ulyana"],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Leskov, Leonid S."],["dc.contributor.author","Sokolov, Andrey V."],["dc.contributor.author","Yevshin, Ivan"],["dc.contributor.author","Mandrik, Nikita"],["dc.contributor.author","Stelmashenko, Daria"],["dc.contributor.author","Koschmann, Jeannette"],["dc.contributor.author","Kel-Margoulis, Olga"],["dc.contributor.author","Krull, Mathias"],["dc.contributor.author","Martínez-Cardús, Anna"],["dc.contributor.author","Moran, Sebastian"],["dc.contributor.author","Esteller, Manel"],["dc.contributor.author","Kolpakov, Fedor"],["dc.contributor.author","Filipenko, Maxim"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2020-12-10T18:38:50Z"],["dc.date.available","2020-12-10T18:38:50Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1186/s12859-019-2687-7"],["dc.identifier.eissn","1471-2105"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16259"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77451"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Walking pathways with positive feedback loops reveal DNA methylation biomarkers of colorectal cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1002958"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Borlak, Juergen"],["dc.date.accessioned","2018-11-07T09:27:25Z"],["dc.date.available","2018-11-07T09:27:25Z"],["dc.date.issued","2013"],["dc.description.abstract","Algorithmic comparison of DNA sequence motifs is a problem in bioinformatics that has received increased attention during the last years. Its main applications concern characterization of potentially novel motifs and clustering of a motif collection in order to remove redundancy. Despite growing interest in motif clustering, the question which motif clusters to aim at has so far not been systematically addressed. Here we analyzed motif similarities in a comprehensive set of vertebrate transcription factor classes. For this we developed enhanced similarity scores by inclusion of the information coverage (IC) criterion, which evaluates the fraction of information an alignment covers in aligned motifs. A network-based method enabled us to identify motif clusters with high correspondence to DNA-binding domain phylogenies and prior experimental findings. Based on this analysis we derived a set of motif families representing distinct binding specificities. These motif families were used to train a classifier which was further integrated into a novel algorithm for unsupervised motif clustering. Application of the new algorithm demonstrated its superiority to previously published methods and its ability to reproduce entrained motif families. As a result, our work proposes a probabilistic approach to decide whether two motifs represent common or distinct binding specificities."],["dc.identifier.doi","10.1371/journal.pcbi.1002958"],["dc.identifier.isi","000316864200030"],["dc.identifier.pmid","23555204"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8739"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/30532"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1553-7358"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","A Discriminative Approach for Unsupervised Clustering of DNA Sequence Motifs"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["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.artnumber","124"],["dc.bibliographiccitation.journal","BMC Systems Biology"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Krull, Mathias"],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T08:39:16Z"],["dc.date.available","2018-11-07T08:39:16Z"],["dc.date.issued","2010"],["dc.description.abstract","Background: The study of relationships between human diseases provides new possibilities for biomedical research. Recent achievements on human genetic diseases have stimulated interest to derive methods to identify disease associations in order to gain further insight into the network of human diseases and to predict disease genes. Results: Using about 10000 manually collected causal disease/gene associations, we developed a statistical approach to infer meaningful associations between human morbidities. The derived method clustered cardiometabolic and endocrine disorders, immune system-related diseases, solid tissue neoplasms and neurodegenerative pathologies into prominent disease groups. Analysis of biological functions confirmed characteristic features of corresponding disease clusters. Inference of disease associations was further employed as a starting point for prediction of disease genes. Efforts were made to underpin the validity of results by relevant literature evidence. Interestingly, many inferred disease relationships correspond to known clinical associations and comorbidities, and several predicted disease genes were subjects of therapeutic target research. Conclusions: Causal molecular mechanisms present a unifying principle to derive methods for disease classification, analysis of clinical disorder associations, and prediction of disease genes. According to the definition of causal disease genes applied in this study, these results are not restricted to genetic disease/gene relationships. This may be particularly useful for the study of long-term or chronic illnesses, where pathological derangement due to environmental or as part of sequel conditions is of importance and may not be fully explained by genetic background."],["dc.description.sponsorship","European Union [LSHM-CT-2006-518153, 200754]"],["dc.identifier.doi","10.1186/1752-0509-4-124"],["dc.identifier.isi","000282260400001"],["dc.identifier.pmid","20815942"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6022"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/18956"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1752-0509"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Molecular mechanistic associations of human diseases"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","W541"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","W545"],["dc.bibliographiccitation.volume","34"],["dc.contributor.author","Waleev, T."],["dc.contributor.author","Shtokalo, D."],["dc.contributor.author","Konovalova, T."],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Cheremushkin, E."],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Kel, Alexander E."],["dc.date.accessioned","2018-11-07T09:39:04Z"],["dc.date.available","2018-11-07T09:39:04Z"],["dc.date.issued","2006"],["dc.description.abstract","Composite Module Analyst (CMA) is a novel software tool aiming to identify promoter-enhancer models based on the composition of transcription factor (TF) binding sites and their pairs. CMA is closely interconnected with the TRANSFAC (R) database. In particular, CMA uses the positional weight matrix (PWM) library collected in TRANSFAC (R) and therefore provides the possibility to search for a large variety of different TF binding sites. We model the structure of the long gene regulatory regions by a Boolean function that joins several local modules, each consisting of co-localized TF binding sites. Having as an input a set of co-regulated genes, CMA builds the promoter model and optimizes the parameters of the model automatically by applying a genetic-regression algorithm. We use a multicomponent fitness function of the algorithm which includes several statistical criteria in a weighted linear function. We show examples of successful application of CMA to a microarray data on transcription profiling of TNF-alpha stimulated primary human endothelial cells. The CMA web server is freely accessible at http://www.gene-regulation.com/pub/programs/cma/CMA.html. An advanced version of CMA is also a part of the commercial system ExPlain (TM) (www.biobase.de) designed for causal analysis of gene expression data."],["dc.identifier.doi","10.1093/nar/gkl342"],["dc.identifier.isi","000245650200108"],["dc.identifier.pmid","16845066"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/4123"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33202"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","0305-1048"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Composite Module Analyst: identification of transcription factor binding site combinations using genetic algorithm"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0258623"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","PLoS One"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Alachram, Halima"],["dc.contributor.author","Chereda, Hryhorii"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Stegmaier, Philip"],["dc.date.accessioned","2022-04-01T10:03:10Z"],["dc.date.available","2022-04-01T10:03:10Z"],["dc.date.issued","2021"],["dc.description.abstract","Biomedical and life science literature is an essential way to publish experimental results. With the rapid growth of the number of new publications, the amount of scientific knowledge represented in free text is increasing remarkably. There has been much interest in developing techniques that can extract this knowledge and make it accessible to aid scientists in discovering new relationships between biological entities and answering biological questions. Making use of the word2vec approach, we generated word vector representations based on a corpus consisting of over 16 million PubMed abstracts. We developed a text mining pipeline to produce word2vec embeddings with different properties and performed validation experiments to assess their utility for biomedical analysis. An important pre-processing step consisted in the substitution of synonymous terms by their preferred terms in biomedical databases. Furthermore, we extracted gene-gene networks from two embedding versions and used them as prior knowledge to train Graph-Convolutional Neural Networks (CNNs) on large breast cancer gene expression data and on other cancer datasets. Performances of resulting models were compared to Graph-CNNs trained with protein-protein interaction (PPI) networks or with networks derived using other word embedding algorithms. We also assessed the effect of corpus size on the variability of word representations. Finally, we created a web service with a graphical and a RESTful interface to extract and explore relations between biomedical terms using annotated embeddings. Comparisons to biological databases showed that relations between entities such as known PPIs, signaling pathways and cellular functions, or narrower disease ontology groups correlated with higher cosine similarity. Graph-CNNs trained with word2vec-embedding-derived networks performed sufficiently good for the metastatic event prediction tasks compared to other networks. Such performance was good enough to validate the utility of our generated word embeddings in constructing biological networks. Word representations as produced by text mining algorithms like word2vec, therefore are able to capture biologically meaningful relations between entities. Our generated embeddings are publicly available at https://github.com/genexplain/Word2vec-based-Networks/blob/main/README.md ."],["dc.description.sponsorship","Bundesministerium für Bildung und Forschung"],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.1371/journal.pone.0258623"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/106100"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-530"],["dc.relation.eissn","1932-6203"],["dc.relation.orgunit","Institut für Medizinische Bioinformatik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Text mining-based word representations for biomedical data analysis and protein-protein interaction networks in machine learning tasks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e17738"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Meier, Tatiana"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Borlak, Juergen"],["dc.date.accessioned","2018-11-07T08:58:00Z"],["dc.date.available","2018-11-07T08:58:00Z"],["dc.date.issued","2011"],["dc.description.abstract","The molecular causes by which the epidermal growth factor receptor tyrosine kinase induces malignant transformation are largely unknown. To better understand EGFs' transforming capacity whole genome scans were applied to a transgenic mouse model of liver cancer and subjected to advanced methods of computational analysis to construct de novo gene regulatory networks based on a combination of sequence analysis and entrained graph-topological algorithms. Here we identified transcription factors, processes, key nodes and molecules to connect as yet unknown interacting partners at the level of protein-DNA interaction. Many of those could be confirmed by electromobility band shift assay at recognition sites of gene specific promoters and by western blotting of nuclear proteins. A novel cellular regulatory circuitry could therefore be proposed that connects cell cycle regulated genes with components of the EGF signaling pathway. Promoter analysis of differentially expressed genes suggested the majority of regulated transcription factors to display specificity to either the pre-tumor or the tumor state. Subsequent search for signal transduction key nodes upstream of the identified transcription factors and their targets suggested the insulin-like growth factor pathway to render the tumor cells independent of EGF receptor activity. Notably, expression of IGF2 in addition to many components of this pathway was highly upregulated in tumors. Together, we propose a switch in autocrine signaling to foster tumor growth that was initially triggered by EGF and demonstrate the knowledge gain form promoter analysis combined with upstream key node identification."],["dc.identifier.doi","10.1371/journal.pone.0017738"],["dc.identifier.isi","000289053800002"],["dc.identifier.pmid","21464922"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8203"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/23539"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 2.5"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.5"],["dc.title","Advanced Computational Biology Methods Identify Molecular Switches for Malignancy in an EGF Mouse Model of Liver Cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]