Publications

Leonie Lampe, Hans-Jürgen Huppertz, Sarah Anderl-Straub, Franziska Albrecht, Tommaso Ballarini, Sandrine Bisenius, Karsten Mueller, Sebastian Niehaus, Klaus Fassbender, Klaus Fliessbach, Holger Jahn, Johannes Kornhuber, Martin Lauer, Johannes Prudlo, Anja Schneider, Matthis Synofzik, Jan Kassubek, Adrian Danek, Arno Villringer, Janine Diehl-Schmid, Markus Otto, Matthias L Schroeter, and FTLD Consortium Germany. Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging. NeuroImage Clin., (37)103320:103320, Elsevier BV, January 2023. [PUMA: topic_neuroinspired Dementia; Diagnosis; MRI; Machine Neurodegeneration; Volumetry learning; topic_lifescience unit_test]

Mariia Tkachenko, Claire Chalopin, Boris Jansen-Winkeln, Thomas Neumuth, Ines Gockel, and Marianne Maktabi. Impact of pre- and post-processing steps for supervised classification of colorectal cancer in hyperspectral images. Cancers (Basel), (15)7April 2023. [PUMA: topic_lifescience cancer cancer; classification; colorectal convolutional filter; hyperspectral imaging; learning; machine median networks; post-processing; pre-processing]

David Nam, Julius Chapiro, Valerie Paradis, Tobias Paul Seraphin, and Jakob Nikolas Kather. Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep., (4)4:100443, Elsevier BV, April 2022. [PUMA: topic_lifescience AI, Artificial CNN, Communications DICOM, Diagnosis; Digital HCC, Imaging Individual ML, MVI, Medicine; NAFLD, NASH, Prognosis Reporting TACE, TRIPOD, Transparent WSIs, a and artificial carcinoma; chemoembolisation; convolutional data deep diagnostic disease; fatty for hepatocellular images; imaging; in integration intelligence; invasion; learning; liver machine microvascular model multimodal multivariable network; neural non-alcoholic of or prediction slide steatohepatitis; support system; transarterial whole]

Souhrid Mukherjee, Joy D Cogan, John H Newman, John A Phillips, 3rd, Rizwan Hamid, Undiagnosed Diseases Network, Jens Meiler, and John A Capra. Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network. Am. J. Hum. Genet., (108)10:1946--1963, Elsevier BV, October 2021. [PUMA: Diseases Network; UDN; Undiagnosed clinical digenic disease disease; learning; machine oligogenic prediction; rare topic_lifescience]

Najia Ahmadi, Quang Vu Nguyen, Martin Sedlmayr, and Markus Wolfien. A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data. Scientific reports, (14)1Nature Publishing Group, Jan 27, 2024. [PUMA: Databases, Electronic FIS_scads Factual, Health Humans, Informatics, Learning, Machine Medical Records topic_lifescience]

Katja Hoffmann, Yuan Peng, Tobias Schlosser, Gabriel Stolze, Holger Langner, Marcel Susky, Trixy Meyer, Marc Ritter, Danny Kowerko, Vinodh Kakkassery, Markus Wolfien, and Martin Sedlmayr. Towards Standardizing Ophthalmic Data for Seamless Interoperability in Eye Care. Studies in health technology and informatics, (317):139--145, IOS Press, Amsterdam u. a., Aug 30, 2024. [PUMA: topic_lifescience Diseases/therapy, Electronic Eye FIS_scads Germany, Health Humans, Information Interoperability/standards, Learning, Level Machine Ophthalmology Records/standards, Seven/standards,]

Praveen Vasudevan, Markus Wolfien, Heiko Lemcke, Cajetan Immanuel Lang, Anna Skorska, Ralf Gaebel, Anne-Marie Galow, Dirk Koczan, Tobias Lindner, Wendy Bergmann, Brigitte Mueller-Hilke, Brigitte Vollmar, Bernd Joachim Krause, Olaf Wolkenhauer, Gustav Steinhoff, and Robert David. CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation. Genome medicine, (15)1BioMed Central, London, Aug 10, 2023. [PUMA: topic_lifescience Animals, C57BL, Cardiac/metabolism, Cell FIS_scads Immunocompromised, Inbred Infarction, Machine Macrophages, Macrophages/metabolism, Mice, Monocytes/metabolism Myocardial Myocytes, Single-cell, infarction, learning, therapy,]

Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F Sbalzarini, and Christian L. Müller. Stability selection enables robust learning of differential equations from limited noisy data. Proceedings of the Royal Society of London : Series A, Mathematical, physical and engineering sciences, (478)2262Royal Society Publishing, June 2022. [PUMA: topic_lifescience FIS_scads PAR differential equations, learning learning, machine proteins, regression, selection, sparse stability statistical theory]