Publications

Melissa Sanabria, Jonas Hirsch, und Anna R Poetsch. The human genome's vocabulary as proposed by the DNA language model GROVER. bioRxiv, Juli 2023. [PUMA: DNA GROVER Xack language model]

Robert Chlumsky, Juliane Mai, James R Craig, und Bryan A Tolson. Advancement of a blended hydrologic model for robust model performance. März 2023. [PUMA: Advancement Yaff blended hydrologic model performance robust]

Samuel I. Berchuck, Felipe A. Medeiros, Sayan Mukherjee, und Andrea Agazzi. Scalable Bayesian inference for the generalized linear mixed model. 2024. [PUMA: Bayesian Scalable generalized imported inference linear mixed model zno] URL

Timo P. Gros, David Groß, Stefan Gumhold, Jörg Hoffmann, Michaela Klauck, und Marcel Steinmetz. TraceVis: Towards Visualization for Deep Statistical Model Checking. Leveraging Applications of Formal Methods, Verification and Validation: Tools and Trends: 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, Rhodes, Greece, October 20–30, 2020, Proceedings, Part IV, 27–46, Springer-Verlag, Berlin, Heidelberg, 2020. [PUMA: Checking Model Networks Neural Statistical Visualization nopdf] URL

Roman C Maron, Achim Hekler, Sarah Haggenmüller, Christof von Kalle, Jochen S Utikal, Verena Müller, Maria Gaiser, Friedegund Meier, Sarah Hobelsberger, Frank F Gellrich, Mildred Sergon, Axel Hauschild, Lars E French, Lucie Heinzerling, Justin G Schlager, Kamran Ghoreschi, Max Schlaak, Franz J Hilke, Gabriela Poch, Sören Korsing, Carola Berking, Markus V Heppt, Michael Erdmann, Sebastian Haferkamp, Dirk Schadendorf, Wiebke Sondermann, Matthias Goebeler, Bastian Schilling, Jakob N Kather, Stefan Fröhling, Daniel B Lipka, Eva Krieghoff-Henning, und Titus J Brinker. Model soups improve performance of dermoscopic skin cancer classifiers. Eur. J. Cancer, (173):307--316, Elsevier BV, September 2022. [PUMA: topic_lifescience Artificial Calibration; Deep Dermatology; Ensembles; Generalisation; Melanoma; Model Nevus; Robustness intelligence; learning; soups;]

David Nam, Julius Chapiro, Valerie Paradis, Tobias Paul Seraphin, und 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]

Jens Przybilla, Peter Ahnert, Holger Bogatsch, Frank Bloos, Frank M Brunkhorst, SepNet Critical Care Trials Group, Progress Study Group, Michael Bauer, Markus Loeffler, Martin Witzenrath, Norbert Suttorp, und Markus Scholz. Markov state modelling of disease courses and mortality risks of patients with community-acquired pneumonia. J. Clin. Med., (9)2:393, MDPI AG, Februar 2020. [PUMA: Markov SOFA community-acquired continuous-time decision making; medical model model; pneumonia; prognosis; score; sepsis; stochastic]

Jan Gaebel, Hans-Georg Wu, Alexander Oeser, Mario A Cypko, Matthaeus Stoehr, Andreas Dietz, Thomas Neumuth, Stefan Franke, und Steffen Oeltze-Jafra. Modeling and processing up-to-dateness of patient information in probabilistic therapy decision support. Artif. Intell. Med., (104)101842:101842, Elsevier BV, April 2020. [PUMA: Arden Decision Head Medical Therapy and decision delay; logic model modules; neck oncology; support syntax; system;]

Akshay Akshay, Masoud Abedi, Navid Shekarchizadeh, Fiona C Burkhard, Mitali Katoch, Alex Bigger-Allen, Rosalyn M Adam, Katia Monastyrskaya, und Ali Hashemi Gheinani. MLcps: machine learning cumulative performance score for classification problems. Gigascience, (12)Dezember 2022. [PUMA: topic_federatedlearn Python classification evaluation evaluation; learning; machine model package; problems; score unified]