Auer, Florian Johann

[["dc.bibliographiccitation.firstpage","137"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Clinical Radiology"],["dc.bibliographiccitation.lastpage","144"],["dc.bibliographiccitation.volume","65"],["dc.contributor.author","Engelke, C."],["dc.contributor.author","Schmidt, S."],["dc.contributor.author","Auer, F."],["dc.contributor.author","Rummeny, Ernst J."],["dc.contributor.author","Marten, Katharina"],["dc.date.accessioned","2018-11-07T08:46:07Z"],["dc.date.available","2018-11-07T08:46:07Z"],["dc.date.issued","2010"],["dc.description.abstract","AIM: To prospectively assess the value of computer-aided detection (CAD) for the Computed tomography (CT) severity assessment of acute Pulmonary embolism (PE). MATERIALS AND METHODS: CT angiographic scans of 58 PE-positive patients (34-89 years, mean 66 years) were analysed by four observers for PE severity using the Mastora index, and by CAD. Patients were stratified to three PE risk groups and results Compared to an independent reference standard. Interobserver agreement was tested by Bland and Altman and extended kappa (Ke) statistics. Mastora index changes after CAD data review were tested by Wilcoxon signed ranks. RESULTS: CAD detected 343 out of 1118 emboli within given arterial segments and a total of 155 out of 218 polysegmental emboli (segmental vessel-based sensitivity = 30.7%, embolus-based sensitivity = 71.2% false-positive rate = 4.1/scan). Interobserver agreement on PE severity 195% limits of agreement (LOA) = -19.7-7.5% and-5.5-3% for reader pairs I versus 2 and 3 versus 4, respectively was enhanced by consensus with CAD data (LOA = -6.5-5.4% and -3.7-2% for reader pairs I versus 2 and 3 versus 4, respectively). Simultaneously, the percentual scoring errors (PSE) were significantly decreased (PSE = 35.4 +/- 31.8% and 5.1 +/- 8.9% for readers 1/2 and 2/3, respectively, and PSE = 27.6 +/- 31 % and 3.8 +/- 6.2%, respectively, after CAD consensus; p <= 0.005). Misclassifications to PE risk groups occurred in 27.6, 24.1, 5.2, and 5.2% of patients for readers 1-4, respectively, (Ke = 0.74) and were corrected by CAD consensus in 56.3, 36, 33.3, and 33.3% of misclassified patients, respectively (Ke = 0.83; p < 0.05). CONCLUSION: Radiologists may benefit from consensus with CAD data that improve PE severity scores and stratification to PE risk groups. (C) 2009 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved."],["dc.identifier.doi","10.1016/j.crad.2009.10.007"],["dc.identifier.isi","000275091700006"],["dc.identifier.pmid","20103436"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/20611"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","W B Saunders Co Ltd"],["dc.relation.issn","0009-9260"],["dc.title","Does computer-assisted detection of pulmonary emboli enhance severity assessment and risk stratification in acute pulmonary embolism?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1930"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","European Radiology"],["dc.bibliographiccitation.lastpage","1938"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Marten, Katharina"],["dc.contributor.author","Seyfarth, T."],["dc.contributor.author","Auer, F."],["dc.contributor.author","Wiener, E."],["dc.contributor.author","Grillhosl, A."],["dc.contributor.author","Obenauer, Silvia"],["dc.contributor.author","Rummeny, Ernst J."],["dc.contributor.author","Engelke, C."],["dc.date.accessioned","2018-11-07T10:45:09Z"],["dc.date.available","2018-11-07T10:45:09Z"],["dc.date.issued","2004"],["dc.description.abstract","To evaluate the performance of experienced versus inexperienced radiologists in comparison and in consensus with an interactive computer-aided detection (CAD) system for detection of pulmonary nodules. Eighteen consecutive patients (mean age: 62.2 years; range 29-83 years) prospectively underwent routine 16-row multislice computed tomography (MSCT). Four blinded radiologists (experienced: readers 1, 2; inexperienced: readers 3, 4) assessed image data against CAD for pulmonary nodules. Thereafter, consensus readings of readers 1+3, reader 1+CAD and reader 3+CAD were performed. Data were compared against an independent gold standard. Statistical tests used to calculate interobserver agreement, reader performance and nodule size were Kappa, ROC and Mann-Whitney U. CAD and experienced readers outperformed inexperienced readers (Az=0.72, 0.71, 0.73, 0.49 and 0.50 for CAD, readers 1-4, respectively; P<0.05). Performance of reader 1+CAD was superior to single reader and reader 1+3 performances (Az=0.93, 0.72 for reader 1+CAD and reader 1+3 consensus, respectively, P<0.05). Reader 3+CAD did not perform superiorly to experienced readers or CAD (Az=0.79 for reader 3+CAD; P>0.05). Consensus of reader 1+CAD significantly outperformed all other readings, demonstrating a benefit in using CAD as an inexperienced reader replacement. It is questionable whether inexperienced readers can be regarded as adequate for interpretation of pulmonary nodules in consensus with CAD, replacing an experienced radiologist."],["dc.identifier.doi","10.1007/s00330-004-2389-y"],["dc.identifier.isi","000226576500028"],["dc.identifier.pmid","15235812"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/47434"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","0938-7994"],["dc.title","Computer-assisted detection of pulmonary nodules: performance evaluation of an expert knowledge-based detection system in consensus reading with experienced and inexperienced chest radiologists"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Pharmacology"],["dc.bibliographiccitation.volume","12"],["dc.contributor.affiliation","Nietert, Manuel Manfred; \r\n\r\n1\r\nDepartment of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Vinhoven, Liza; \r\n\r\n1\r\nDepartment of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Auer, Florian; \r\n\r\n3\r\nInstitute for Informatics, University of Augsburg, Augsburg, Germany"],["dc.contributor.affiliation","Hafkemeyer, Sylvia; \r\n\r\n4\r\nMukoviszidose Institut gGmbH, Bonn, Germany"],["dc.contributor.affiliation","Stanke, Frauke; \r\n\r\n5\r\nGerman Center for Lung Research (DZL), Partner Site BREATH, Hannover, Germany"],["dc.contributor.author","Nietert, Manuel Manfred"],["dc.contributor.author","Vinhoven, Liza"],["dc.contributor.author","Auer, Florian"],["dc.contributor.author","Hafkemeyer, Sylvia"],["dc.contributor.author","Stanke, Frauke"],["dc.date.accessioned","2022-01-11T14:06:16Z"],["dc.date.available","2022-01-11T14:06:16Z"],["dc.date.issued","2021"],["dc.date.updated","2022-02-09T13:20:22Z"],["dc.description.abstract","Background: Cystic fibrosis (CF) is a genetic disease caused by mutations in CFTR , which encodes a chloride and bicarbonate transporter expressed in exocrine epithelia throughout the body. Recently, some therapeutics became available that directly target dysfunctional CFTR, yet research for more effective substances is ongoing. The database CandActCFTR aims to provide detailed and comprehensive information on candidate therapeutics for the activation of CFTR-mediated ion conductance aiding systems-biology approaches to identify substances that will synergistically activate CFTR-mediated ion conductance based on published data. Results: Until 10/2020, we derived data from 108 publications on 3,109 CFTR-relevant substances via the literature database PubMed and further 666 substances via ChEMBL; only 19 substances were shared between these sources. One hundred and forty-five molecules do not have a corresponding entry in PubChem or ChemSpider, which indicates that there currently is no single comprehensive database on chemical substances in the public domain. Apart from basic data on all compounds, we have visualized the chemical space derived from their chemical descriptors via a principal component analysis annotated for CFTR-relevant biological categories. Our online query tools enable the search for most similar compounds and provide the relevant annotations in a structured way. The integration of the KNIME software environment in the back-end facilitates a fast and user-friendly maintenance of the provided data sets and a quick extension with new functionalities, e.g., new analysis routines. CandActBase automatically integrates information from other online sources, such as synonyms from PubChem and provides links to other resources like ChEMBL or the source publications. Conclusion: CandActCFTR aims to establish a database model of candidate cystic fibrosis therapeutics for the activation of CFTR-mediated ion conductance to merge data from publicly available sources. Using CandActBase, our strategy to represent data from several internet resources in a merged and organized form can also be applied to other use cases. For substances tested as CFTR activating compounds, the search function allows users to check if a specific compound or a closely related substance was already tested in the CF field. The acquired information on tested substances will assist in the identification of the most promising candidates for future therapeutics."],["dc.description.abstract","Background: Cystic fibrosis (CF) is a genetic disease caused by mutations in CFTR , which encodes a chloride and bicarbonate transporter expressed in exocrine epithelia throughout the body. Recently, some therapeutics became available that directly target dysfunctional CFTR, yet research for more effective substances is ongoing. The database CandActCFTR aims to provide detailed and comprehensive information on candidate therapeutics for the activation of CFTR-mediated ion conductance aiding systems-biology approaches to identify substances that will synergistically activate CFTR-mediated ion conductance based on published data. Results: Until 10/2020, we derived data from 108 publications on 3,109 CFTR-relevant substances via the literature database PubMed and further 666 substances via ChEMBL; only 19 substances were shared between these sources. One hundred and forty-five molecules do not have a corresponding entry in PubChem or ChemSpider, which indicates that there currently is no single comprehensive database on chemical substances in the public domain. Apart from basic data on all compounds, we have visualized the chemical space derived from their chemical descriptors via a principal component analysis annotated for CFTR-relevant biological categories. Our online query tools enable the search for most similar compounds and provide the relevant annotations in a structured way. The integration of the KNIME software environment in the back-end facilitates a fast and user-friendly maintenance of the provided data sets and a quick extension with new functionalities, e.g., new analysis routines. CandActBase automatically integrates information from other online sources, such as synonyms from PubChem and provides links to other resources like ChEMBL or the source publications. Conclusion: CandActCFTR aims to establish a database model of candidate cystic fibrosis therapeutics for the activation of CFTR-mediated ion conductance to merge data from publicly available sources. Using CandActBase, our strategy to represent data from several internet resources in a merged and organized form can also be applied to other use cases. For substances tested as CFTR activating compounds, the search function allows users to check if a specific compound or a closely related substance was already tested in the CF field. The acquired information on tested substances will assist in the identification of the most promising candidates for future therapeutics."],["dc.identifier.doi","10.3389/fphar.2021.689205"],["dc.identifier.eissn","1663-9812"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/97866"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-507"],["dc.relation.eissn","1663-9812"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Comprehensive Analysis of Chemical Structures That Have Been Tested as CFTR Activating Substances in a Publicly Available Database CandActCFTR"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","42"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Genome Medicine"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Chereda, Hryhorii"],["dc.contributor.author","Bleckmann, Annalen"],["dc.contributor.author","Menck, Kerstin"],["dc.contributor.author","Perera-Bel, Júlia"],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Auer, Florian"],["dc.contributor.author","Kramer, Frank"],["dc.contributor.author","Leha, Andreas"],["dc.contributor.author","Beißbarth, Tim"],["dc.date.accessioned","2021-04-14T08:28:07Z"],["dc.date.accessioned","2022-08-18T12:39:45Z"],["dc.date.available","2021-04-14T08:28:07Z"],["dc.date.available","2022-08-18T12:39:45Z"],["dc.date.issued","2021-03-11"],["dc.date.updated","2022-07-29T12:18:05Z"],["dc.description.abstract","Abstract\r\n \r\n Background\r\n Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. However, the machine learning community made recent elaborations on interpretability methods explaining data point-specific decisions of deep learning techniques. We believe that such explanations can assist the need in personalized precision medicine decisions via explaining patient-specific predictions.\r\n \r\n \r\n Methods\r\n Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-Euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g., distant metastasis in cancer, for each individual patient.\r\n \r\n \r\n Results\r\n We extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset and then apply the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression.\r\n \r\n \r\n Conclusions\r\n The developed method could be potentially highly useful on interpreting classification results in the context of different omics data and prior knowledge molecular networks on the individual patient level, as for example in precision medicine approaches or a molecular tumor board."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.citation","Genome Medicine. 2021 Mar 11;13(1):42"],["dc.identifier.doi","10.1186/s13073-021-00845-7"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17744"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82506"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112973"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","BioMed Central"],["dc.relation.eissn","1756-994X"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject","Gene expression data"],["dc.subject","Explainable AI"],["dc.subject","Personalized medicine"],["dc.subject","Precision medicine"],["dc.subject","Classification of cancer"],["dc.subject","Deep learning"],["dc.subject","Prior knowledge"],["dc.subject","Molecular networks"],["dc.title","Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","716"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","717"],["dc.bibliographiccitation.volume","34"],["dc.contributor.author","Auer, Florian"],["dc.contributor.author","Hammoud, Zaynab"],["dc.contributor.author","Ishkin, Alexandr"],["dc.contributor.author","Pratt, Dexter"],["dc.contributor.author","Ideker, Trey"],["dc.contributor.author","Kramer, Frank"],["dc.contributor.editor","Kelso, Janet"],["dc.date.accessioned","2020-12-10T18:17:34Z"],["dc.date.available","2020-12-10T18:17:34Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1093/bioinformatics/btx683"],["dc.identifier.eissn","1460-2059"],["dc.identifier.issn","1367-4803"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/75078"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","ndexr—an R package to interface with the network data exchange"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]