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Over ons Praktische zaken Waar vindt u ons M. (Michael) Biehl, Prof

Research interests

Machine Learning, Neural Networks, Data Analysis, Pattern Recognition, Biomedical Data, Bioinformatics, Classification, Supervised Learning, Unsupervised Learning, Computational Science, Scientific Computation, Modeling and Simulation, Statistical Physics

Publicaties

Hidden unit specialization in layered neural networks:: ReLU vs. sigmoidal activation

Urine steroid metabolomics for the differential diagnosis of adrenal incidentalomas in the EURINE-ACT study: A prospective test validation study

Tissue- and development-stage-specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples

An application of generalized matrix learning vector quantization in neuroimaging

Supervised learning in the presence of concept drift: A modelling framework

Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces

Subspace corrected relevance learning with application in neuroimaging

Translating the potential of the urine steroid metabolome to stage NAFLD (TrUSt-NAFLD): Study protocol for a multicentre, prospective validation study

Urine steroid metabolomics as a diagnostic tool in primary aldosteronism

20th SC@RUG 2023 proceedings 2022-2023

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