Hauschild, Anne-Christin

[["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.firstpage","344"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Metabolites"],["dc.bibliographiccitation.lastpage","363"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Frisch, Tobias"],["dc.contributor.author","Baumbach, Jörg Ingo"],["dc.contributor.author","Baumbach, Jan"],["dc.date.accessioned","2021-09-17T08:41:42Z"],["dc.date.available","2021-09-17T08:41:42Z"],["dc.date.issued","2015-06-10"],["dc.description.abstract","Computational breath analysis is a growing research area aiming at identifying volatile organic compounds (VOCs) in human breath to assist medical diagnostics of the next generation. While inexpensive and non-invasive bioanalytical technologies for metabolite detection in exhaled air and bacterial/fungal vapor exist and the first studies on the power of supervised machine learning methods for profiling of the resulting data were conducted, we lack methods to extract hidden data features emerging from confounding factors. Here, we present Carotta, a new cluster analysis framework dedicated to uncovering such hidden substructures by sophisticated unsupervised statistical learning methods. We study the power of transitivity clustering and hierarchical clustering to identify groups of VOCs with similar expression behavior over most patient breath samples and/or groups of patients with a similar VOC intensity pattern. This enables the discovery of dependencies between metabolites. On the one hand, this allows us to eliminate the effect of potential confounding factors hindering disease classification, such as smoking. On the other hand, we may also identify VOCs associated with disease subtypes or concomitant diseases. Carotta is an open source software with an intuitive graphical user interface promoting data handling, analysis and visualization. The back-end is designed to be modular, allowing for easy extensions with plugins in the future, such as new clustering methods and statistics. It does not require much prior knowledge or technical skills to operate. We demonstrate its power and applicability by means of one artificial dataset. We also apply Carotta exemplarily to a real-world example dataset on chronic obstructive pulmonary disease (COPD). While the artificial data are utilized as a proof of concept, we will demonstrate how Carotta finds candidate markers in our real dataset associated with confounders rather than the primary disease (COPD) and bronchial carcinoma (BC). Carotta is publicly available at http://carotta.compbio.sdu.dk [1]."],["dc.identifier.doi","10.3390/metabo5020344"],["dc.identifier.pmid","26065494"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89616"],["dc.language.iso","en"],["dc.relation.issn","2218-1989"],["dc.title","Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","218"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Journal of Integrative Bioinformatics"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Schneider, Till"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Baumbach, Jörg Ingo"],["dc.contributor.author","Baumbach, Jan"],["dc.date.accessioned","2021-09-17T08:41:20Z"],["dc.date.available","2021-09-17T08:41:20Z"],["dc.date.issued","2013-04-02"],["dc.description.abstract","Over the last decade the evaluation of odors and vapors in human breath has gained more and more attention, particularly in the diagnostics of pulmonary diseases. Ion mobility spectrometry coupled with multi-capillary columns (MCC/IMS), is a well known technology for detecting volatile organic compounds (VOCs) in air. It is a comparatively inexpensive, non-invasive, high-throughput method, which is able to handle the moisture that comes with human exhaled air, and allows for characterizing of VOCs in very low concentrations. To identify discriminating compounds as biomarkers, it is necessary to have a clear understanding of the detailed composition of human breath. Therefore, in addition to the clinical studies, there is a need for a flexible and comprehensive centralized data repository, which is capable of gathering all kinds of related information. Moreover, there is a demand for automated data integration and semi-automated data analysis, in particular with regard to the rapid data accumulation, emerging from the high-throughput nature of the MCC/IMS technology. Here, we present a comprehensive database application and analysis platform, which combines metabolic maps with heterogeneous biomedical data in a well-structured manner. The design of the database is based on a hybrid of the entity-attribute-value (EAV) model and the EAV-CR, which incorporates the concepts of classes and relationships. Additionally it offers an intuitive user interface that provides easy and quick access to the platform’s functionality: automated data integration and integrity validation, versioning and roll-back strategy, data retrieval as well as semi-automatic data mining and machine learning capabilities. The platform will support MCC/IMS-based biomarker identification and validation. The software, schemata, data sets and further information is publicly available at http://imsdb.mpi-inf.mpg.de."],["dc.identifier.doi","10.2390/biecoll-jib-2013-218"],["dc.identifier.pmid","23545212"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89612"],["dc.language.iso","en"],["dc.relation.eissn","1613-4516"],["dc.title","An integrative clinical database and diagnostics platform for biomarker identification and analysis in ion mobility spectra of human exhaled air"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","277"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Metabolites"],["dc.bibliographiccitation.lastpage","293"],["dc.bibliographiccitation.volume","3"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Kopczynski, Dominik"],["dc.contributor.author","D'Addario, Marianna"],["dc.contributor.author","Baumbach, Jörg Ingo"],["dc.contributor.author","Rahmann, Sven"],["dc.contributor.author","Baumbach, Jan"],["dc.date.accessioned","2021-09-17T08:41:36Z"],["dc.date.available","2021-09-17T08:41:36Z"],["dc.date.issued","2013-04-16"],["dc.description.abstract","Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors' results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications."],["dc.identifier.doi","10.3390/metabo3020277"],["dc.identifier.pmid","24957992"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89615"],["dc.language.iso","en"],["dc.relation.issn","2218-1989"],["dc.title","Peak detection method evaluation for ion mobility spectrometry by using machine learning approaches"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]