Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.
2021CLEP: A hybrid data- and knowledge-driven framework for generating patient representations
Bharadhwaj, Vinay Srinivas; Ali, Mehdi; Birkenbihl, Colin; Mubeen, Sarah; Lehmann, Jens; Hofmann-Apitius, Martin; Hoyt, Charles Tapley; Domingo-Fernández, Daniel
2021An Explainable Multimodal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data
Linden, Thoma; Jong, J. de; Lu, C.; Kiri, V.; Haeffs, K.; Fröhlich, H.
2021SEEDS: Data driven inference of structural model errors and unknown inputs for dynamic systems biology
Newmiwaka, Tobias; Engelhardt, Benjamin; Wendland, Philipp; Kahl, Dominik; Fröhlich, Holger; Kschischo, Maik
2021Towards realizing the vision of precision medicine: AI based prediction of clinical drug response
Jong, Johann de; Cutcutache, Ioana; Page, Matthew; Elmoufti, Sami; Dilley, Cynthia; Fröhlich, Holger; Armstrong, Martin
2020Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia - lessons for translation into clinical practice
Birkenbihl, Colin; Emon, Mohammad Asif; Vrooman, Henri; Westwood, Sarah; Lovestone, Simon; Hofmann-Apitius, Martin; Fröhlich, Holger
2020In silico signaling modeling to understand cancer pathways and treatment responses
Kunz, Meik; Jeromin, Julian; Fuchs, Maximilian; Christoph, Jan; Veronesi, Giulia; Flentje, Michael; Nietzer, Sarah; Dandekar, Gudrun; Dandekar, Thomas
2020Optimal multiparametric set-up modelled for best survival outcomes in palliative treatment of liver malignancies
Goldstein, Elisha; Yeghiazaryan, Kristina; Ahmad, Ashar; Giordano, Frank A.; Fröhlich, Holger; Golubnitschaja, Olga
2020PathME: Pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data
Lemsara, Amina; Ouadfel, Salima; Fröhlich, Holger