Perera-Bel, Júlia

[["dc.contributor.author","Kurz, Nadine S."],["dc.contributor.author","Perera-Bel, Júlia"],["dc.contributor.author","Höltermann, Charlotte"],["dc.contributor.author","Tucholski, Tim"],["dc.contributor.author","Yang, Jingyu"],["dc.contributor.author","Beissbarth, Tim"],["dc.contributor.author","Dönitz, Jürgen"],["dc.contributor.editor","Röhrig, Rainer"],["dc.contributor.editor","Grabe, Niels"],["dc.contributor.editor","Hoffmann, Verena S."],["dc.contributor.editor","Hübner, Ursula"],["dc.contributor.editor","König, Jochem"],["dc.contributor.editor","Sax, Ulrich"],["dc.contributor.editor","Schreiweis, Björn"],["dc.contributor.editor","Sedlmayr, Martin"],["dc.date.accessioned","2022-10-04T10:21:42Z"],["dc.date.available","2022-10-04T10:21:42Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.3233/SHTI220806"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/114480"],["dc.notes.intern","DOI-Import GROB-600"],["dc.publisher","IOS Press"],["dc.relation.crisseries","Studies in Health Technology and Informatics"],["dc.relation.eisbn","9781643683034"],["dc.relation.isbn","9781643683027"],["dc.relation.ispartof","German Medical Data Sciences 2022 – Future Medicine: More Precise, More Integrative, More Sustainable! : Proceedings of the Joint Conference of the 67th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) and the 14th Annual Meeting of the TMF – Technology, Methods, and Infrastructure for Networked Medical Research e.V. 2022 online in Kiel, Germany"],["dc.title","Identifying Actionable Variants in Cancer – The Dual Web and Batch Processing Tool MTB-Report"],["dc.type","book_chapter"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","18"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Genome Medicine"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Perera-Bel, Júlia"],["dc.contributor.author","Hutter, Barbara"],["dc.contributor.author","Heining, Christoph"],["dc.contributor.author","Bleckmann, Annalen"],["dc.contributor.author","Fröhlich, Martina"],["dc.contributor.author","Fröhling, Stefan"],["dc.contributor.author","Glimm, Hanno"],["dc.contributor.author","Brors, Benedikt"],["dc.contributor.author","Beißbarth, Tim"],["dc.date.accessioned","2019-01-25T08:41:44Z"],["dc.date.available","2019-01-25T08:41:44Z"],["dc.date.issued","2018"],["dc.description.abstract","A comprehensive understanding of cancer has been furthered with technological improvements and decreasing costs of next-generation sequencing (NGS). However, the complexity of interpreting genomic data is hindering the implementation of high-throughput technologies in the clinical context: increasing evidence on gene-drug interactions complicates the task of assigning clinical significance to genomic variants."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2018"],["dc.identifier.doi","10.1186/s13073-018-0529-2"],["dc.identifier.pmid","29544535"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15160"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/57380"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","In goescholar not merged with http://resolver.sub.uni-goettingen.de/purl?gs-1/15091 but duplicate"],["dc.notes.status","zu prüfen"],["dc.relation.eissn","1756-994X"],["dc.rights","CC BY 4.0"],["dc.rights.access","openAccess"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.title","From somatic variants towards precision oncology: Evidence-driven reporting of treatment options in molecular tumor boards"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","217"],["dc.bibliographiccitation.lastpage","221"],["dc.contributor.author","Wolff, Alexander"],["dc.contributor.author","Perera-Bel, Júlia"],["dc.contributor.author","Schildhaus, Hans-Ulrich"],["dc.contributor.author","Homayounfar, Kia"],["dc.contributor.author","Schatlo, Bawarjan"],["dc.contributor.author","Bleckmann, Annalen"],["dc.contributor.author","Beißbarth, Tim"],["dc.date.accessioned","2020-03-27T09:43:47Z"],["dc.date.available","2020-03-27T09:43:47Z"],["dc.date.issued","2018"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63383"],["dc.relation.eisbn","978-1-61499-896-9"],["dc.relation.isbn","978-1-61499-895-2"],["dc.relation.ispartof","German Medical Data Sciences: A Learning Healthcare System"],["dc.relation.ispartofseries","Studies in health technology and informatics;253"],["dc.title","Using RNA-Seq Data for the Detection of a Panel of Clinically Relevant Mutations"],["dc.type","book_chapter"],["dc.type.internalPublication","yes"],["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","11"],["dc.bibliographiccitation.lastpage","19"],["dc.contributor.author","Perera-Bel, Júlia"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.editor","Duttge, Gunnar"],["dc.contributor.editor","Sax, Ulrich"],["dc.contributor.editor","Schweda, Mark"],["dc.contributor.editor","Umbach, Nadine"],["dc.date.accessioned","2020-03-27T08:17:35Z"],["dc.date.available","2020-03-27T08:17:35Z"],["dc.date.issued","2019"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63378"],["dc.language.iso","en"],["dc.publisher","Mohr Siebeck"],["dc.publisher.place","Tübingen"],["dc.relation.eisbn","978-3-16-156189-4"],["dc.relation.isbn","978-3-16-155861-0"],["dc.relation.ispartof","Next-generation medicine : ethische, rechtliche und technologische Fragen genomischer Hochdurchsatzdaten in der klinischen Praxis"],["dc.title","Molecular diagnosis and therapy prediction using biomarkers in the era of next-generation sequencing"],["dc.type","book_chapter"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]