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Artificial intelligence reveals mechanism behind brain tumour - Uppsala University, Sweden

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Researchers at Uppsala University have used computer modelling to study how brain tumours arise. The study, which is published today in the journal EBioMedicine, illustrated how researchers in the future will be able to use large-scale data to find new disease mechanisms and identify new treatment targets. The last ten years' progress in molecular biology has drastically changed how cancer researchers work. Instead of almost exclusively using different biological models, like cells, today large-scale statistical analyses are increasingly used to understand tumour diseases and find new therapies. Researchers at Uppsala University, together with colleagues at the University of Gothenburg, Chalmers University of Technology and University of Freiburg, have developed a new algorithm, aSICS, that uses large amounts of data to suggest hypotheses about "what causes what" in a cancer cell.


Attractive Innovation Project Awards 2019 - UU Innovation - Uppsala University, Sweden

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Common to all projects is support från Uppsala University Innovation and success in securing external funding to further enhance development opportunities. Proteins are the workers of the cell, and many proteins interact with each other. In order to understand the importance of these interactions, there is a need to measure both free and interacting proteins. Ola Söderberg, professor at the Department of Pharmaceutical Biosciences, has developed a method to label each protein with its own unique colour, making it possible to measure the proteins individually. At the same time, the proportion of proteins that bind to each other are labelled with a combination of the colours.


artificial-intelligence-reveals-mechanism-behind-brain-tumor#.V-HVw5TwthI.twitter

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Researchers at Uppsala University have used computer modeling to study how brain tumors arise. Instead of almost exclusively using different biological models, like cells, today large-scale statistical analyses are increasingly used to understand tumor diseases and find new therapies. In the study the researchers used aSICS to interpret data from brain tumors and they could identify a new mechanism behind mesenchymal glioblastoma, an extra aggressive brain tumor type. "According to the computer model, mesenchymal glioblastoma is partly caused by alterations in a gene called Annexin A2.


Guest posting: Building a PhenoMeNal metabolomics e-infrastructure - GigaBlog

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The European Union's Horizon 2020 research and innovation programme is funding the PhenoMeNal (Phenome and Metabolome aNalysis) project that aims to support data processing and analysis pipelines for molecular phenotype data generated from metabolomics applications. We aim to build an open, community-led and community-supported e-infrastructure by leveraging existing cloud infrastructures, tooling, and data repositories, under one umbrella of services dedicated to the European biomedical community to begin with, and eventually worldwide. However, the experience and tooling can be applied to non clinical setting. The project was formulated to address the complexity and high volumes of data being generated, collected, and analysed that are quickly going beyond current data management and computational capabilities. For example, it is estimated that a single National Phenome Centre managing only around 100,000 human samples per year might generate a velocity of data amounting to more than 2PB annually.


Guest posting: Building a PhenoMeNal metabolomics e-infrastructure - GigaBlog

#artificialintelligence

The European Union's Horizon 2020 research and innovation programme is funding the PhenoMeNal (Phenome and Metabolome aNalysis) project that aims to support data processing and analysis pipelines for molecular phenotype data generated from metabolomics applications. We aim to build an open, community-led and community-supported e-infrastructure by leveraging existing cloud infrastructures, tooling, and data repositories, under one umbrella of services dedicated to the European biomedical community to begin with, and eventually worldwide. However, the experience and tooling can be applied to non clinical settings. The project was formulated to address the complexity and high volumes of data being generated, collected, and analysed that are quickly going beyond current data management and computational capabilities. For example, it is estimated that a single National Phenome Centre managing only around 100,000 human samples per year might generate a velocity of data amounting to more than 2PB annually.