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potential applications. In particular, we focus on evolutionary prediction: can we use a deeper understanding of evolvability to predict and, potentially, control evolutionary processes? Read more about our
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“Quantitative predictions of protein – DNA interactions from high-throughput biophysical binding data”. Sequence specific binding and recognition between transcription factors and DNA control gene expression at
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year project, funded by the DDLS program, we aim to develop AI-based tools in design of affinity ligands, such as the prediction of binding interactions between proteins. Data-driven life science (DDLS
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gene expression profiles and cellular heterogeneity within tissues can predict how existing drugs might act on previously uncharacterized disease mechanisms or cellular subtypes. These models will be
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time and space and create AI-based models to predict human cells. As the Scientific Program Manager, you will make a key contribution to this exciting new direction at SciLifeLab through setting up
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Cell initiative is a new flagship research program aiming to develop an AI model of a human cell to predict key cellular functions. It is funded by the Knut & Alice Wallenberg Foundation (KAW) and
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new flagship research program aiming to to map the molecular structure and function of single human cells in time and space and create AI-based models to predict human cells. It is funded by the Knut
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of the workplace The Genetic and Molecular Epidemiology Unit at the Department of Clinical Sciences conducts research primarily on data-driven solutions in precision medicine, with focus on precision prediction
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. David Marlevi, Prof. Ulf Hedin, and Dr. Ljubica Matic to improve stroke risk prediction for patients with carotid atherosclerosis using a multidisciplinary combination of data-driven imaging
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to build sequence dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming