Publications

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

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: 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]

Bian Li, Jeffrey Mendenhall, John A Capra, and 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: convolutional deep learning; long memory multitask networks; neural prediction prediction; secondary short-term structure topic_lifescience topology transmembrane]

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, and 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, March 2022. [PUMA: AI; LNM adipose and artificial biomarker; bowel cancer; colorectal deep digital early inflamed intelligence; learning; metastasis; new pT1 pT2 pathology; prediction predictive tissue;]

Kaitlyn V Ledwitch, Georg Künze, Jacob R McKinney, Elleansar Okwei, Katherine Larochelle, Lisa Pankewitz, Soumya Ganguly, Heather L Darling, Irene Coin, and 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, June 2023. [PUMA: (IMPs); (PCSs); (ncAAs); Click Integral Non-canonical Pseudocontact Rosetta; Structure acids amino chemistry; membrane prediction proteins shifts topic_lifescience]

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

Ariel Iporre-Rivas, Dorothee Saur, Karl Rohr, Gerik Scheuermann, and 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, July 2023. [PUMA: graph imaging; learning; machine medical multi-modal networks; neural prediction stroke]

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