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Retrospectively validated AI model allows early detection of Alzheimer's six years in advance.
AI approaches allow efficient prioritization of potential drug targets.
AI allows for accurate prediction of adverse drug events and target-associated phenotypes.
Innovative AI approach allows identification of prognostically relevant molecular cancer subtypes based on multi-omics data.
Innovative AI approach allows the generation of realistic synthetic patient trajectories.
AI allows for stratification of Alzheimer and Parkinson patients via molecular disease mechanisms – a step towards Precision medicine in neurology.
New AI approach allows for the clustering of multivariate longitudinal patient trajectories and detection of progression subtypes in Alzheimer's and Parkinson's Disease.
Explainable AI techniques allow for predicting health-related outcomes (comorbidities, disease severity) from large real-world data.
Towards realizing the vision of precision medicine: AI allows for predicting response to anti-epileptic drugs.
AI Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases.
Scientific Machine Learning combines human knowledge with data to predict pharmacokinetics of drugs:
Causal machine learning techniques allow estimation of intervention effects based on observational data:
Fraunhofer Institute for Algorithms and Scientific Computing SCAI