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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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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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life science. The aim of this PhD position is to develop a novel phylogenetic approach to predict unknown species interactions. For that, the student will compile all available data on host use
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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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evaluating designed backbones and predicting the functional effects of protein variants. In addition, the doctoral student will be part of the DDLS initiative, and participate in the DDLS Research School
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immunity and develop diagnostic approaches that accurately predict therapy benefit and enable successful individualized cancer therapy planning. Contemporary AI-based approaches show great promise to advance
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or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale experimental data are now available or it may use population-scale genetic, clinical, or public health
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clinical prediction of progression remains difficult, leading to over- and undertreatment of women with particularly early breast cancer. Over the past decade, spatial tissue analysis techniques have been