Hauschild, Anne-Christin

[["dc.bibliographiccitation.firstpage","e30"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","e30"],["dc.bibliographiccitation.volume","50"],["dc.contributor.author","Löchel, Hannah F"],["dc.contributor.author","Welzel, Marius"],["dc.contributor.author","Hattab, Georges"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Heider, Dominik"],["dc.date.accessioned","2022-06-08T07:59:10Z"],["dc.date.available","2022-06-08T07:59:10Z"],["dc.date.issued","2021"],["dc.description.abstract","Abstract The use of complex biological molecules to solve computational problems is an emerging field at the interface between biology and computer science. There are two main categories in which biological molecules, especially DNA, are investigated as alternatives to silicon-based computer technologies. One is to use DNA as a storage medium, and the other is to use DNA for computing. Both strategies come with certain constraints. In the current study, we present a novel approach derived from chaos game representation for DNA to generate DNA code words that fulfill user-defined constraints, namely GC content, homopolymers, and undesired motifs, and thus, can be used to build codes for reliable DNA storage systems."],["dc.identifier.doi","10.1093/nar/gkab1209"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110655"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.eissn","1362-4962"],["dc.relation.issn","0305-1048"],["dc.title","Fractal construction of constrained code words for DNA storage systems"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","105534"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","iScience"],["dc.bibliographiccitation.volume","25"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Martin, Roman"],["dc.contributor.author","Holst, Sabrina Celine"],["dc.contributor.author","Wienbeck, Joachim"],["dc.contributor.author","Heider, Dominik"],["dc.date.accessioned","2022-12-01T08:30:38Z"],["dc.date.available","2022-12-01T08:30:38Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1016/j.isci.2022.105534"],["dc.identifier.pii","S2589004222018065"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/117938"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-621"],["dc.relation.issn","2589-0042"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","Guideline for software life cycle in health informatics"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Journal of Integrative Bioinformatics"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Barbosa, Eudes"],["dc.contributor.author","Röttger, Richard"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","de Castro Soares, Siomar"],["dc.contributor.author","Böcker, Sebastian"],["dc.contributor.author","Azevedo, Vasco"],["dc.contributor.author","Baumbach, Jan"],["dc.date.accessioned","2021-09-17T08:41:11Z"],["dc.date.available","2021-09-17T08:41:11Z"],["dc.date.issued","2017-07-05"],["dc.description.abstract","Distinct bacteria are able to cope with highly diverse lifestyles; for instance, they can be free living or host-associated. Thus, these organisms must possess a large and varied genomic arsenal to withstand different environmental conditions. To facilitate the identification of genomic features that might influence bacterial adaptation to a specific niche, we introduce LifeStyle-Specific-Islands (LiSSI). LiSSI combines evolutionary sequence analysis with statistical learning (Random Forest with feature selection, model tuning and robustness analysis). In summary, our strategy aims to identify conserved consecutive homology sequences (islands) in genomes and to identify the most discriminant islands for each lifestyle."],["dc.identifier.doi","10.1515/jib-2017-0010"],["dc.identifier.pmid","28678736"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89610"],["dc.language.iso","en"],["dc.relation.issn","1613-4516"],["dc.title","LifeStyle-Specific-Islands (LiSSI): Integrated Bioinformatics Platform for Genomic Island Analysis"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","PA3693"],["dc.contributor.author","Dauletbaev, Nurlan"],["dc.contributor.author","Akik, Wided"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Damm, Thomas"],["dc.contributor.author","Suleimenova, Assel"],["dc.contributor.author","Oshibayeva, Ainash"],["dc.contributor.author","Kalmatayeva, Zhanna"],["dc.contributor.author","Vogelmeier, Claus"],["dc.contributor.author","Lands, Larry"],["dc.date.accessioned","2022-06-08T07:57:15Z"],["dc.date.available","2022-06-08T07:57:15Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1183/13993003.congress-2021.PA3693"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110038"],["dc.notes.intern","DOI-Import GROB-575"],["dc.publisher","European Respiratory Society"],["dc.relation.conference","ERS International Congress 2021 abstracts"],["dc.title","Multidimensional scaling in Euclidean space of cytokine responses to document hyperinflammation in cystic fibrosis cells"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Translational Psychiatry"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Marshe, Victoria S."],["dc.contributor.author","Maciukiewicz, Malgorzata"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Islam, Farhana"],["dc.contributor.author","Qin, Li"],["dc.contributor.author","Tiwari, Arun K."],["dc.contributor.author","Sibille, Etienne"],["dc.contributor.author","Blumberger, Daniel M."],["dc.contributor.author","Karp, Jordan F."],["dc.contributor.author","Flint, Alastair J."],["dc.contributor.author","Müller, Daniel J."],["dc.date.accessioned","2022-06-08T07:58:41Z"],["dc.date.available","2022-06-08T07:58:41Z"],["dc.date.issued","2021"],["dc.description.abstract","Abstract Antidepressant outcomes in older adults with depression is poor, possibly because of comorbidities such as cerebrovascular disease. Therefore, we leveraged multiple genome-wide approaches to understand the genetic architecture of antidepressant response. Our sample included 307 older adults (≥60 years) with current major depression, treated with venlafaxine extended-release for 12 weeks. A standard genome-wide association study (GWAS) was conducted for post-treatment remission status, followed by in silico biological characterization of associated genes, as well as polygenic risk scoring for depression, neurodegenerative and cerebrovascular disease. The top-associated variants for remission status and percentage symptom improvement were PIEZO1 rs12597726 ( OR  = 0.33 [0.21, 0.51], p  = 1.42 × 10 −6 ) and intergenic rs6916777 ( Beta  = 14.03 [8.47, 19.59], p  = 1.25 × 10 −6 ), respectively. Pathway analysis revealed significant contributions from genes involved in the ubiquitin-proteasome system, which regulates intracellular protein degradation with has implications for inflammation, as well as atherosclerotic cardiovascular disease ( n  = 25 of 190 genes, p  = 8.03 × 10 −6 , FDR-corrected p  = 0.01). Given the polygenicity of complex outcomes such as antidepressant response, we also explored 11 polygenic risk scores associated with risk for Alzheimer’s disease and stroke. Of the 11 scores, risk for cardioembolic stroke was the second-best predictor of non-remission, after being male (Accuracy = 0.70 [0.59, 0.79], Sensitivity = 0.72, Specificity = 0.67; p  = 2.45 × 10 −4 ). Although our findings did not reach genome-wide significance, they point to previously-implicated mechanisms and provide support for the roles of vascular and inflammatory pathways in LLD. Overall, significant enrichment of genes involved in protein degradation pathways that may be impaired, as well as the predictive capacity of risk for cardioembolic stroke, support a link between late-life depression remission and risk for vascular dysfunction."],["dc.identifier.doi","10.1038/s41398-021-01248-3"],["dc.identifier.pii","1248"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110496"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.eissn","2158-3188"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Genome-wide analysis suggests the importance of vascular processes and neuroinflammation in late-life antidepressant response"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","325"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","334"],["dc.bibliographiccitation.volume","38"],["dc.contributor.author","Ren, Yunxiao"],["dc.contributor.author","Chakraborty, Trinad"],["dc.contributor.author","Doijad, Swapnil"],["dc.contributor.author","Falgenhauer, Linda"],["dc.contributor.author","Falgenhauer, Jane"],["dc.contributor.author","Goesmann, Alexander"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Schwengers, Oliver"],["dc.contributor.author","Heider, Dominik"],["dc.contributor.editor","Birol, Inanc"],["dc.date.accessioned","2022-06-08T07:59:05Z"],["dc.date.available","2022-06-08T07:59:05Z"],["dc.date.issued","2021"],["dc.description.abstract","Abstract Motivation Antimicrobial resistance (AMR) is one of the biggest global problems threatening human and animal health. Rapid and accurate AMR diagnostic methods are thus very urgently needed. However, traditional antimicrobial susceptibility testing (AST) is time-consuming, low throughput and viable only for cultivable bacteria. Machine learning methods may pave the way for automated AMR prediction based on genomic data of the bacteria. However, comparing different machine learning methods for the prediction of AMR based on different encodings and whole-genome sequencing data without previously known knowledge remains to be done. Results In this study, we evaluated logistic regression (LR), support vector machine (SVM), random forest (RF) and convolutional neural network (CNN) for the prediction of AMR for the antibiotics ciprofloxacin, cefotaxime, ceftazidime and gentamicin. We could demonstrate that these models can effectively predict AMR with label encoding, one-hot encoding and frequency matrix chaos game representation (FCGR encoding) on whole-genome sequencing data. We trained these models on a large AMR dataset and evaluated them on an independent public dataset. Generally, RFs and CNNs perform better than LR and SVM with AUCs up to 0.96. Furthermore, we were able to identify mutations that are associated with AMR for each antibiotic. Availability and implementation Source code in data preparation and model training are provided at GitHub website (https://github.com/YunxiaoRen/ML-iAMR). Supplementary information Supplementary data are available at Bioinformatics online."],["dc.identifier.doi","10.1093/bioinformatics/btab681"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110630"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.eissn","1460-2059"],["dc.relation.issn","1367-4803"],["dc.title","Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","102803"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","iScience"],["dc.bibliographiccitation.volume","24"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Eick, Lisa"],["dc.contributor.author","Wienbeck, Joachim"],["dc.contributor.author","Heider, Dominik"],["dc.date.accessioned","2022-06-08T07:57:54Z"],["dc.date.available","2022-06-08T07:57:54Z"],["dc.date.issued","2021"],["dc.description.sponsorship"," http://dx.doi.org/10.13039/100014028 H2020"],["dc.description.sponsorship"," http://dx.doi.org/10.13039/501100000780 European Commission"],["dc.description.sponsorship"," http://dx.doi.org/10.13039/100010661 Horizon 2020 Framework Programme"],["dc.description.sponsorship"," http://dx.doi.org/10.13039/501100007601 Horizon 2020"],["dc.identifier.doi","10.1016/j.isci.2021.102803"],["dc.identifier.pii","S2589004221007719"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110248"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.issn","2589-0042"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","Fostering reproducibility, reusability, and technology transfer in health informatics"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","733"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Metabolites"],["dc.bibliographiccitation.lastpage","755"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Schneider, Till"],["dc.contributor.author","Pauling, Josch"],["dc.contributor.author","Rupp, Kathrin"],["dc.contributor.author","Jang, Mi"],["dc.contributor.author","Baumbach, Jörg Ingo"],["dc.contributor.author","Baumbach, Jan"],["dc.date.accessioned","2021-09-17T08:41:31Z"],["dc.date.available","2021-09-17T08:41:31Z"],["dc.date.issued","2012-10-16"],["dc.description.abstract","Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain."],["dc.identifier.doi","10.3390/metabo2040733"],["dc.identifier.pmid","24957760"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89614"],["dc.language.iso","en"],["dc.relation.issn","2218-1989"],["dc.title","Computational methods for metabolomic data analysis of ion mobility spectrometry data-reviewing the state of the art"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","1047760"],["dc.bibliographiccitation.journal","Frontiers in Genetics"],["dc.bibliographiccitation.volume","13"],["dc.contributor.affiliation","Batra, Richa; \r\n1\r\nDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States"],["dc.contributor.affiliation","Baloni, Priyanka; \r\n2\r\nSchool of Health Sciences, Purdue University, West Lafayette, IN, United States"],["dc.contributor.affiliation","Alcaraz, Nicolas; \r\n3\r\nNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark"],["dc.contributor.affiliation","Hauschild, Anne-Christin; \r\n4\r\nDepartment of Medical Informatics, University Medical Center Göttingen, Georg-August University of Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Cervera, Alejandra; \r\n5\r\nInstituto Nacional de Medicina Genómica (INMEGEN), Mexico City, Mexico"],["dc.contributor.author","Batra, Richa"],["dc.contributor.author","Baloni, Priyanka"],["dc.contributor.author","Alcaraz, Nicolas"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Cervera, Alejandra"],["dc.date.accessioned","2022-12-01T08:31:30Z"],["dc.date.available","2022-12-01T08:31:30Z"],["dc.date.issued","2022"],["dc.date.updated","2022-11-11T13:12:37Z"],["dc.identifier.doi","10.3389/fgene.2022.1047760"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/118188"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-621"],["dc.relation.eissn","1664-8021"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Editorial: Computational systems biomedicine"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","117"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Anesthesiology"],["dc.bibliographiccitation.lastpage","126"],["dc.bibliographiccitation.volume","122"],["dc.contributor.author","Fink, Tobias"],["dc.contributor.author","Wolf, Alexander"],["dc.contributor.author","Maurer, Felix"],["dc.contributor.author","Albrecht, Frederic W."],["dc.contributor.author","Heim, Nathalie"],["dc.contributor.author","Wolf, Beate"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Bödeker, Bertram"],["dc.contributor.author","Baumbach, Jörg I."],["dc.contributor.author","Volk, Thomas"],["dc.contributor.author","Sessler, Daniel I."],["dc.contributor.author","Kreuer, Sascha"],["dc.date.accessioned","2021-09-17T08:40:57Z"],["dc.date.available","2021-09-17T08:40:57Z"],["dc.date.issued","2015-01"],["dc.description.abstract","Multicapillary column ion-mobility spectrometry (MCC-IMS) may identify volatile components in exhaled gas. The authors therefore used MCC-IMS to evaluate exhaled gas in a rat model of sepsis, inflammation, and hemorrhagic shock."],["dc.identifier.doi","10.1097/ALN.0000000000000420"],["dc.identifier.pmid","25170570"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89607"],["dc.language.iso","en"],["dc.relation.eissn","1528-1175"],["dc.relation.issn","0003-3022"],["dc.title","Volatile organic compounds during inflammation and sepsis in rats: a potential breath test using ion-mobility spectrometry"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]