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]
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]
Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med. Image Anal., (79)102474:102474, Elsevier BV, Juli 2022. [PUMA: Convolutional Artificial intelligence; Multiple-Instance deep neural networks; learning Weakly-supervised Learning; transformers; pathology; Vision Computational]
Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach. Heliyon, (9)7:e17890, Elsevier BV, Juli 2023. [PUMA: neural Precision networks; identification; Recurrent Transfer learning medicine; Haematopoiesis; unit_transfer System]
Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review. Eur. J. Cancer, (160):80--91, Elsevier BV, Januar 2022. [PUMA: Convolutional Biomarker Multimodal neural networks; Omics identification; topic_lifescience Cancer; fusion;]
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, Februar 2022. [PUMA: discovery; library; cheminformatics; deep network; neural biochemical open-source BCL; drug design; topic_lifescience QSAR;]
Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks. Sensors, (24)42024. [PUMA: Networks Noisy Density Invertible Magnetic Reconstruction Flux Fraction Void Neural] URL
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]
Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks. Sensors (Basel), (21)6:2005, MDPI AG, März 2021. [PUMA: convolutional (SHM) monitoring learning; neural networks; rotors; health dense composites; connected structural composite machine fully]
TraceVis: Towards Visualization for Deep Statistical Model Checking. Leveraging Applications of Formal Methods, Verification and Validation: Tools and Trends: 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, Rhodes, Greece, October 20–30, 2020, Proceedings, Part IV, 27–46, Springer-Verlag, Berlin, Heidelberg, 2020. [PUMA: Networks Visualization Model Statistical Checking Neural] URL