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 topic_lifescience 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]
Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology. J. Hepatol., (77)1:116--127, Elsevier BV, July 2022. [PUMA: image artificial immune intelligence; pathology; gene deep learning; signatures; slide topic_lifescience whole]
Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: A possible preselection for molecular testing?. Eur. Urol. Focus, (8)2:472--479, Elsevier BV, March 2022. [PUMA: Artificial intelligence; learning; therapy for Deep mutations; Bladder cancer; Molecular fibroblast receptor testing topic_lifescience growth factor FGFR3]
Automatic lung segmentation and quantification of aeration in computed tomography of the chest using 3D transfer learning. Front. Physiol., (12):725865, 2021. [PUMA: ARDS; index; recruitment; segmentation; uNet transfer lung deep learning; COVID-19; Jaccard]
Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med. Image Anal., (79)102474:102474, Elsevier BV, July 2022. [PUMA: Convolutional Artificial intelligence; Multiple-Instance deep neural networks; learning Weakly-supervised Learning; transformers; pathology; topic_lifescience Vision Computational]
BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. RNA Biology, (21)1:410–421, Informa UK Limited, March 2024. [PUMA: deep RNA non-coding feature learning extraction BioDeepfuse classification] URL
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: metastasis; inflamed intelligence; deep learning; bowel predictive LNM adipose pT2 pT1 digital biomarker; cancer; artificial AI; pathology; colorectal and topic_lifescience prediction new tissue; early]
Deep learning improves pancreatic cancer diagnosis using RNA-based variants. Cancers (Basel), (13)11:2654, MDPI AG, May 2021. [PUMA: cancer; pancreatitis; study transcriptome-wide deep learning; topic_lifescience association pancreatic chronic]
Deep reinforcement learning with artificial microswimmers. Emerging Topics in Artificial Intelligence (ETAI) 2022, (12204):104--110, 2022. [PUMA: microswimmers artificial learning topic_physchemistry reinforcement Deep]
Insights into the Drivers and Spatiotemporal Trends of Extreme Mediterranean Wildfires with Statistical Deep Learning. Artificial Intelligence for the Earth Systems, (2)4American Meteorological Society, October 2023. [PUMA: Mediterranean Extreme Learning Wildfires Spatiotemporal Trends Statistical Deep] URL
Introduction to the BioChemical Library (BCL): An application-based open-source toolkit for integrated cheminformatics and machine learning in computer-aided drug discovery. Front. Pharmacol., (13):833099, Frontiers Media SA, February 2022. [PUMA: discovery; library; cheminformatics; deep network; neural biochemical open-source BCL; drug design; topic_lifescience QSAR;]
Leptin deficiency-caused behavioral change - A comparative analysis using EthoVision and DeepLabCut. Frontiers in neuroscience, (17)Frontiers Media S.A., Mar 24, 2023. [PUMA: FIS_scads Obesity Behavioral learning, topic_lifescience EthoVision, analysis, DeepLabCut, Deep]
Memory-efficient deep learning on a SpiNNaker 2 prototype. Front. Neurosci., (12):840, Frontiers Media SA, November 2018. [PUMA: footprint; energy parallelism; memory pruning; SpiNNaker; efficient deep hardware; sparsity rewiring;]
Model soups improve performance of dermoscopic skin cancer classifiers. Eur. J. Cancer, (173):307--316, Elsevier BV, September 2022. [PUMA: Artificial intelligence; learning; Deep Melanoma; soups; Dermatology; Robustness Calibration; Ensembles; topic_lifescience Model Generalisation; Nevus;]
Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human disease. eBioMedicine, (108):105312, 2024. [PUMA: Disease Cross-species model, learning Deep matching, Single-cell RNA-seq, topic_mathfoundation COVID-19, analysis, state Hamster] URL
Predicting mutational status of driver and suppressor genes directly from histopathology with Deep Learning: A systematic study across 23 solid tumor types. Front. Genet., (12):806386, 2021. [PUMA: genetic artificail (AI); pathway; deep learning; cancer genes; pathway TCGA; intelligence]
Robust path following on rivers using bootstrapped reinforcement learning. Ocean engineering, (298)Elsevier Science B.V., Apr 15, 2024. [PUMA: FIS_scads Path waterways Autonomous Restricted learning, surface vessel, reinforcement Deep topic_engineering following,]
Self-organized free-flight arrival for urban air mobility. Transportation Research Part C: Emerging Technologies, (167):104806, 2024. [PUMA: Urban mobility eVTOL air learning reinforcement Deep topic_engineering] URL
SetQuence & SetOmic: Deep Set Transformer-based Representations of Cancer Multi-Omics. 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022, 139--147, IEEE, New York u. a., United States of America, 2022. [PUMA: FIS_scads Set processing, gene expression, natural language molecular livinglab genome, Deep sequence topic_federatedlearn multi-omics, mutome, Representations analysis, Network, Neural]
Spatial–temporal recurrent reinforcement learning for autonomous ships. Neural Networks, (2023)165:634--653, Elsevier Science B.V., Jun 15, 2023. [PUMA: FIS_scads Networks, Autonomous surface Computer, Recurrency, Deep vehicle, COLREG, learning, Algorithms, Psychology, Ships, Reward reinforcement topic_engineering Neural Reinforcement,]