Publications

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;]