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.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.contributor.author","Hufsky, Franziska"],["dc.contributor.author","Lamkiewicz, Kevin"],["dc.contributor.author","Almeida, Alexandre"],["dc.contributor.author","Aouacheria, Abdel"],["dc.contributor.author","Arighi, Cecilia"],["dc.contributor.author","Bateman, Alex"],["dc.contributor.author","Baumbach, Jan"],["dc.contributor.author","Beerenwinkel, Niko"],["dc.contributor.author","Brandt, Christian"],["dc.contributor.author","Cacciabue, Marco"],["dc.contributor.author","Chuguransky, Sara"],["dc.contributor.author","Drechsel, Oliver"],["dc.contributor.author","Finn, Robert D."],["dc.contributor.author","Fritz, Adrian"],["dc.contributor.author","Fuchs, Stephan"],["dc.contributor.author","Hattab, Georges"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Heider, Dominik"],["dc.contributor.author","Hoffmann, Marie"],["dc.contributor.author","Hölzer, Martin"],["dc.contributor.author","Hoops, Stefan"],["dc.contributor.author","Kaderali, Lars"],["dc.contributor.author","Kalvari, Ioanna"],["dc.contributor.author","von Kleist, Max"],["dc.contributor.author","Kmiecinski, René"],["dc.contributor.author","Kühnert, Denise"],["dc.contributor.author","Lasso, Gorka"],["dc.contributor.author","Libin, Pieter"],["dc.contributor.author","List, Markus"],["dc.contributor.author","Löchel, Hannah F."],["dc.contributor.author","Martin, Maria J."],["dc.contributor.author","Martin, Roman"],["dc.contributor.author","Matschinske, Julian"],["dc.contributor.author","McHardy, Alice C."],["dc.contributor.author","Mendes, Pedro"],["dc.contributor.author","Mistry, Jaina"],["dc.contributor.author","Navratil, Vincent"],["dc.contributor.author","Nawrocki, Eric"],["dc.contributor.author","O'Toole, Áine Niamh"],["dc.contributor.author","Palacios-Ontiveros, Nancy"],["dc.contributor.author","Petrov, Anton I."],["dc.contributor.author","Rangel-Piñeros, Guillermo"],["dc.contributor.author","Redaschi, Nicole"],["dc.contributor.author","Reimering, Susanne"],["dc.contributor.author","Reinert, Knut"],["dc.contributor.author","Reyes, Alejandro"],["dc.contributor.author","Richardson, Lorna"],["dc.contributor.author","Robertson, David L."],["dc.contributor.author","Sadegh, Sepideh"],["dc.contributor.author","Singer, Joshua B."],["dc.contributor.author","Theys, Kristof"],["dc.contributor.author","Upton, Chris"],["dc.contributor.author","Welzel, Marius"],["dc.contributor.author","Williams, Lowri"],["dc.contributor.author","Marz, Manja"],["dc.date.accessioned","2021-09-17T08:41:15Z"],["dc.date.available","2021-09-17T08:41:15Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.20944/preprints202005.0376.v1"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89611"],["dc.title","Computational Strategies to Combat COVID-19: Useful Tools to Accelerate SARS-CoV-2 and Coronavirus Research"],["dc.type","preprint"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","S0010482522000555"],["dc.bibliographiccitation.firstpage","105263"],["dc.bibliographiccitation.journal","Computers in Biology and Medicine"],["dc.bibliographiccitation.volume","143"],["dc.contributor.author","Beinecke, Jacqueline Michelle"],["dc.contributor.author","Anders, Patrick"],["dc.contributor.author","Schurrat, Tino"],["dc.contributor.author","Heider, Dominik"],["dc.contributor.author","Luster, Markus"],["dc.contributor.author","Librizzi, Damiano"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.date.accessioned","2022-04-01T10:02:23Z"],["dc.date.available","2022-04-01T10:02:23Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1016/j.compbiomed.2022.105263"],["dc.identifier.pii","S0010482522000555"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/105894"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-530"],["dc.relation.issn","0010-4825"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","Evaluation of machine learning strategies for imaging confirmed prostate cancer recurrence prediction on electronic health records"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2278"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","2286"],["dc.bibliographiccitation.volume","38"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Lemanczyk, Marta"],["dc.contributor.author","Matschinske, Julian"],["dc.contributor.author","Frisch, Tobias"],["dc.contributor.author","Zolotareva, Olga"],["dc.contributor.author","Holzinger, Andreas"],["dc.contributor.author","Baumbach, Jan"],["dc.contributor.author","Heider, Dominik"],["dc.contributor.editor","Wren, Jonathan"],["dc.date.accessioned","2022-06-08T07:59:06Z"],["dc.date.available","2022-06-08T07:59:06Z"],["dc.date.issued","2022"],["dc.description.abstract","Abstract Motivation Limited data access has hindered the field of precision medicine from exploring its full potential, e.g. concerning machine learning and privacy and data protection rules. Our study evaluates the efficacy of federated Random Forests (FRF) models, focusing particularly on the heterogeneity within and between datasets. We addressed three common challenges: (i) number of parties, (ii) sizes of datasets and (iii) imbalanced phenotypes, evaluated on five biomedical datasets. Results The FRF outperformed the average local models and performed comparably to the data-centralized models trained on the entire data. With an increasing number of models and decreasing dataset size, the performance of local models decreases drastically. The FRF, however, do not decrease significantly. When combining datasets of different sizes, the FRF vastly improve compared to the average local models. We demonstrate that the FRF remain more robust and outperform the local models by analyzing different class-imbalances. Our results support that FRF overcome boundaries of clinical research and enables collaborations across institutes without violating privacy or legal regulations. Clinicians benefit from a vast collection of unbiased data aggregated from different geographic locations, demographics and other varying factors. They can build more generalizable models to make better clinical decisions, which will have relevance, especially for patients in rural areas and rare or geographically uncommon diseases, enabling personalized treatment. In combination with secure multi-party computation, federated learning has the power to revolutionize clinical practice by increasing the accuracy and robustness of healthcare AI and thus paving the way for precision medicine. Availability and implementation The implementation of the federated random forests can be found at https://featurecloud.ai/. Supplementary information Supplementary data are available at Bioinformatics online."],["dc.identifier.doi","10.1093/bioinformatics/btac065"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110631"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.eissn","1460-2059"],["dc.relation.issn","1367-4803"],["dc.title","Federated Random Forests can improve local performance of predictive models for various healthcare applications"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","101297"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","iScience"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Martin, Roman"],["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","2021-09-17T08:40:20Z"],["dc.date.available","2021-09-17T08:40:20Z"],["dc.date.issued","2020-07-24"],["dc.description.abstract","Since the outbreak in 2019, researchers are trying to find effective drugs against the SARS-CoV-2 virus based on de novo drug design and drug repurposing. The former approach is very time consuming and needs extensive testing in humans, whereas drug repurposing is more promising, as the drugs have already been tested for side effects, etc. At present, there is no treatment for COVID-19 that is clinically effective, but there is a huge amount of data from studies that analyze potential drugs. We developed CORDITE to efficiently combine state-of-the-art knowledge on potential drugs and make it accessible to scientists and clinicians. The web interface also provides access to an easy-to-use API that allows a wide use for other software and applications, e.g., for meta-analysis, design of new clinical studies, or simple literature search. CORDITE is currently empowering many scientists across all continents and accelerates research in the knowledge domains of virology and drug design."],["dc.identifier.doi","10.1016/j.isci.2020.101297"],["dc.identifier.pmid","32619700"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89600"],["dc.language.iso","en"],["dc.relation.eissn","2589-0042"],["dc.relation.issn","2589-0042"],["dc.title","CORDITE: The Curated CORona Drug InTERactions Database for SARS-CoV-2"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]