Publications

Sahar Vahdati, Deepankan Bharathi Nagaraj, Maximilian Bryan, Sobhan Moazemi, Sabine Gründer-Fahrer, and Michael Martin. Utilizing transformers on OCT imagery and metadata for treatment response prediction in macular edema patients. Lecture Notes in Computer Science, 3--15, Springer Nature Switzerland, Cham, 2023. [PUMA: OCT Xack edema macular prediction response topic_lifescience transformers treatment]

Ilyes Abdelhamid, Alessandro Muscoloni, Danny Marc Rotscher, Matthias Lieber, Ulf Markwardt, and Carlo Vittorio Cannistraci. Network shape intelligence outperforms AlphaFold2 intelligence in vanilla protein interaction prediction. bioRxiv, August 2023. [PUMA: AlphaFold2 Xack intelligence interaction prediction protein vanilla]

Oliver Kirsten, Martin Bogdan, and Sophie Adama. Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient. 2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC), 37-41, 2023. [PUMA: Complexity Computational Consciousness Information Learning Locked-In Machine Measures Modeling Neuroscience Prediction Predictive Processing Signal Syndrome Theory Training Zno algorithms and data learning modeling models processing]

Dustyn Eggers, Christian Höner zu Siederdissen, and Peter F. Stadler. Accuracy of RNA Structure Prediction Depends on the Pseudoknot Grammar. Advances in Bioinformatics and Computational Biology, 20–31, Springer Nature Switzerland, 2022. [PUMA: DependsPseudoknot Grammar Prediction RNA Structure] URL

Veronia Iskandar, Mohamed A.Abd El Ghany, and Diana Goehringer. Performance Estimation and Prototyping of Reconfigurable Near-Memory Computing Systems. In Nele Mentens, Nele Mentens, Leonel Sousa, Pedro Trancoso, Nikela Papadopoulou, and Ioannis Sourdis (Eds.), Proceedings - 2023 33rd International Conference on Field-Programmable Logic and Applications, FPL 2023, 357--358, Institute of Electrical and Electronics Engineers Inc., United States of America, Nov 2, 2023. [PUMA: topic_federatedlearn Architectures, Computing, FIS_scads High Near Parallel Prediction bandwidth memories, memory] URL

Veronia Iskandar, Mohamed A.Abd El Ghany, and Diana Goehringer. Compiler-Assisted Kernel Selection for FPGA-based Near-Memory Computing Platforms. Proceedings - 31st IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2023, 222, Institute of Electrical and Electronics Engineers Inc., United States of America, 2023. [PUMA: topic_federatedlearn FIS_scads High-bandwidth architectures, characterization, computing, memory, near-memory parallel prediction] URL

Dimitra Kiakou, Adam Adamopoulos, and 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: topic_neuroinspired Disease Feature Graph-Based Impact Investigating Neuroimaging Prediction Selection learning]

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

Lummy Maria Monteiro, Joao Saraiva, Rodolfo Toscan, Peter Stadler, Rafael Silva-Rocha, and Ulisses Nunes da Rocha. PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning. Environmental Microbiome, (17)December 2022. [PUMA: PredicTF: bacterial complex learning microbialdeep prediction transcription]

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: topic_lifescience AI; LNM adipose and artificial biomarker; bowel cancer; colorectal deep digital early inflamed intelligence; learning; metastasis; new pT1 pT2 pathology; prediction predictive tissue;]

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]

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: topic_visualcomputing graph imaging; learning; machine medical multi-modal networks; neural prediction stroke]

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: topic_lifescience (IMPs); (PCSs); (ncAAs); Click Integral Non-canonical Pseudocontact Rosetta; Structure acids amino chemistry; membrane prediction proteins shifts]