Publications

Scarlet Brockmoeller, Amelie Echle, Narmin Ghaffari Laleh, Susanne Eiholm, Marie Louise Malmstrøm, Tine Plato Kuhlmann, Katarina Levic, Heike Irmgard Grabsch, Nicholas P West, Oliver Lester Saldanha, Katerina Kouvidi, Aurora Bono, Lara R Heij, Titus J Brinker, Ismayil Gögenür, Philip Quirke, und Jakob Nikolas Kather. Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer. J. Pathol., (256)3:269--281, Wiley, März 2022. [PUMA: metastasis; inflamed intelligence; deep learning; bowel predictive LNM adipose pT2 pT1 digital biomarker; cancer; artificial AI; pathology; colorectal and prediction new tissue; early]

Patrick Ebel, Ibrahim Emre Göl, Christoph Lingenfelder, und Andreas Vogelsang. Destination Prediction Based on Partial Trajectory Data. 2020. [PUMA: Destination Partial Prediction Data Trajectory Based on] URL

Ariel Iporre-Rivas, Dorothee Saur, Karl Rohr, Gerik Scheuermann, und Christina Gillmann. Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network. J. Med. Imaging (Bellingham), (10)4:044502, SPIE-Intl Soc Optical Eng, Juli 2023. [PUMA: stroke medical learning; neural networks; imaging; machine prediction multi-modal graph]

Dimitra Kiakou, Adam Adamopoulos, und Nico Scherf. Graph-Based Disease Prediction in Neuroimaging: Investigating the Impact of Feature Selection. Worldwide Congress on “Genetics, Geriatrics and Neurodegenerative Diseases Research", 223--230, 2022. [PUMA: Neuroimaging Investigating Selection Impact Disease Graph-Based Prediction learning Feature]

Kaitlyn V Ledwitch, Georg Künze, Jacob R McKinney, Elleansar Okwei, Katherine Larochelle, Lisa Pankewitz, Soumya Ganguly, Heather L Darling, Irene Coin, und Jens Meiler. Sparse pseudocontact shift NMR data obtained from a non-canonical amino acid-linked lanthanide tag improves integral membrane protein structure prediction. J. Biomol. NMR, (77)3:69--82, Juni 2023. [PUMA: membrane Click proteins (IMPs); acids Non-canonical amino (ncAAs); topic_lifescience (PCSs); shifts prediction Structure Integral Pseudocontact Rosetta; chemistry;]

Bian Li, Jeffrey Mendenhall, John A Capra, und Jens Meiler. A multitask deep-learning method for predicting membrane associations and secondary structures of proteins. J. Proteome Res., (20)8:4089--4100, American Chemical Society (ACS), August 2021. [PUMA: multitask convolutional prediction; memory transmembrane deep learning; neural networks; topology long short-term structure secondary topic_lifescience prediction]

Eli Fritz McDonald, Taylor Jones, Lars Plate, Jens Meiler, und Alican Gulsevin. Benchmarking AlphaFold2 on peptide structure prediction. Structure, (31)1:111--119.e2, Elsevier BV, Januar 2023. [PUMA: bonds; benchmark; folding; peptides; AlphaFold2; disulfide protein topic_lifescience prediction pLDDT; structure]

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: a disease; HCC, or intelligence; liver multivariable non-alcoholic multimodal ML, artificial WSIs, images; prediction support integration TACE, DICOM, AI, network; deep neural Digital Diagnosis; MVI, transarterial fatty microvascular convolutional in learning; imaging; Communications invasion; NAFLD, chemoembolisation; hepatocellular Reporting steatohepatitis; Transparent Individual slide machine Prognosis Artificial data for NASH, whole Medicine; TRIPOD, of CNN, and Imaging carcinoma; diagnostic system; model]

Davide Sala, Peter W Hildebrand, und Jens Meiler. Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties. Front. Mol. Biosci., (10):1121962, Februar 2023. [PUMA: (G-protein-coupled receptors); protein GPCRs kinases; topic_lifescience prediction function; AlphaFold; structure]