Schmitt, Armin

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[["dc.bibliographiccitation.firstpage","562"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","International Journal of Molecular Sciences"],["dc.bibliographiccitation.volume","22"],["dc.contributor.affiliation","Rajavel, Abirami; \t\t \r\n\t\t Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany, abirami.rajavel@uni-goettingen.de"],["dc.contributor.affiliation","Schmitt, Armin Otto; \t\t \r\n\t\t Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany, armin.schmitt@uni-goettingen.de\t\t \r\n\t\t Center for Integrated Breeding Research (CiBreed), Albrecht-Thaer-Weg 3, Georg-August University, 37075 Göttingen, Germany, armin.schmitt@uni-goettingen.de"],["dc.contributor.affiliation","Gültas, Mehmet; \t\t \r\n\t\t Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany, gueltas@informatik.uni-goettingen.de\t\t \r\n\t\t Center for Integrated Breeding Research (CiBreed), Albrecht-Thaer-Weg 3, Georg-August University, 37075 Göttingen, Germany, gueltas@informatik.uni-goettingen.de"],["dc.contributor.author","Rajavel, Abirami"],["dc.contributor.author","Schmitt, Armin Otto"],["dc.contributor.author","Gültas, Mehmet"],["dc.date.accessioned","2021-04-14T08:29:43Z"],["dc.date.available","2021-04-14T08:29:43Z"],["dc.date.issued","2021"],["dc.date.updated","2022-09-06T03:41:43Z"],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3390/ijms22020562"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82969"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1422-0067"],["dc.relation.orgunit","Tierärztliches Institut"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Computational Identification of Master Regulators Influencing Trypanotolerance in Cattle"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","892"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Genes"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Ramzan, Faisal"],["dc.contributor.author","Gültas, Mehmet"],["dc.contributor.author","Bertram, Hendrik"],["dc.contributor.author","Cavero, David"],["dc.contributor.author","Schmitt, Armin Otto"],["dc.date.accessioned","2021-04-14T08:27:20Z"],["dc.date.available","2021-04-14T08:27:20Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.3390/genes11080892"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17504"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82252"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","MDPI"],["dc.relation.eissn","2073-4425"],["dc.relation.haserratum","/handle/2/83490"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Combining Random Forests and a Signal Detection Method Leads to the Robust Detection of Genotype-Phenotype Associations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","464"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Genes"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Ramzan, Faisal"],["dc.contributor.author","Klees, Selina"],["dc.contributor.author","Schmitt, Armin Otto"],["dc.contributor.author","Cavero, David"],["dc.contributor.author","Gültas, Mehmet"],["dc.date.accessioned","2021-04-14T08:26:24Z"],["dc.date.available","2021-04-14T08:26:24Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.3390/genes11040464"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17412"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81929"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","MDPI"],["dc.relation.eissn","2073-4425"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Identification of Age-Specific and Common Key Regulatory Mechanisms Governing Eggshell Strength in Chicken Using Random Forests"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","264"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Physiological Genomics"],["dc.bibliographiccitation.lastpage","270"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Wagener, Asja"],["dc.contributor.author","Schmitt, Armin O."],["dc.contributor.author","Aksu, Soner"],["dc.contributor.author","Schlote, Werner"],["dc.contributor.author","Neuschl, Christina"],["dc.contributor.author","Brockmann, Gudrun A."],["dc.date.accessioned","2022-06-08T07:57:07Z"],["dc.date.available","2022-06-08T07:57:07Z"],["dc.date.issued","2006"],["dc.description.abstract","Mouse lines long-term selected for high fatness offer the possibility to identify individual genes involved in the development of obesity. The Berlin Fat Mouse (BFM) line has been selected for low protein content and afterward for high fatness. Three Berlin Fat Mouse Inbred (BFMI) lines, which are derivates of the selection line BFM and an unselected control line (C57BL/6; B6) were systematically phenotyped between 3 and 20 wk. The body weights and body compositions were measured on a weekly basis. We demonstrated that the BFMI lines dispose of more body weight, body fat mass, and body lean mass than the control line B6 because of a better feed efficiency in these lines. In contrast to other growth-selected mouse lines, the BFMI lines exhibited a general increase in body fat mass but only a marginal increase in body lean mass. The three BFMI lines also showed line- and sex-specific patterns and varied in their response to high-fat diet. The phenotypic differences between the BFMI lines can be traced back to different sets of fixed alleles contributing to fat accumulation and diet-induced obesity. Our results demonstrate that the genetically related BFMI lines are novel models to study the genetic as well as the nutritional aspects of obesity."],["dc.identifier.doi","10.1152/physiolgenomics.00225.2005"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110004"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.eissn","1531-2267"],["dc.relation.issn","1094-8341"],["dc.title","Genetic, sex, and diet effects on body weight and obesity in the Berlin Fat Mouse Inbred lines"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Wiese, Lena"],["dc.contributor.author","Wangmo, Chimi"],["dc.contributor.author","Steuernagel, Lukas"],["dc.contributor.author","Schmitt, Armin O."],["dc.contributor.author","Gültas, Mehmet"],["dc.date.accessioned","2020-05-25T13:25:09Z"],["dc.date.available","2020-05-25T13:25:09Z"],["dc.date.issued","2018"],["dc.description.abstract","Genome analysis is a major precondition for future advances in the life sciences. The complex organization of genome data and the interactions between genomic components can often be modeled and visualized in graph structures. In this paper we propose the integration of several data sets into a graph database. We study the aptness of the database system in terms of analysis and visualization of a genome regulatory network (GRN) by running a benchmark on it. Major advantages of using a database system are the modifiability of the data set, the immediate visualization of query results as well as built-in indexing and caching features."],["dc.identifier.doi","10.5446/38888"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/65961"],["dc.language.iso","en"],["dc.relation.conference","Data Integration in the Life Sciences (DILS2018)"],["dc.relation.date","2018"],["dc.relation.eventlocation","Hannover"],["dc.title","Construction and Visualization of Dynamic Biological Networks: Benchmarking the Neo4J Graph Database"],["dc.type","lecture"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

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[["dc.bibliographiccitation.firstpage","e4821"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Schmitt, Armin"],["dc.contributor.author","Schuchhardt, Johannes"],["dc.contributor.author","Brockmann, Gudrun A."],["dc.contributor.editor","Jones, Dafydd"],["dc.date.accessioned","2022-06-08T07:57:25Z"],["dc.date.available","2022-06-08T07:57:25Z"],["dc.date.issued","2009"],["dc.identifier.doi","10.1371/journal.pone.0004821"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110085"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.eissn","1932-6203"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","The Action of Key Factors in Protein Evolution at High Temporal Resolution"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]