Chan Zuckerberg Initiative grant to the Glastonbury Group
The Glastonbury Group is among the recipients of the Data Insights Cycle 3 awards. The aim of the grant is to develop a machine learning model that identifies disease-relevant cell subpopulations whilst predicting a phenotype/disease of interest from large-scale single-cell RNA-seq data.
Understanding the pathogenesis of SARS-CoV-2 encephalitis
In collaboration with an international team of scientists, HT researchers identified a missense mutation in a gene involved in brain-intrinsic immunity as the genetic cause of SARS-CoV-2 brainstem encephalitis.
Machine learning reveals hidden features in histology images
Researchers at Human Technopole developed a self-supervised machine learning model that combines histology, gene expression, and genetic variation to automatically identify and cluster distinct tissue substructures, cells, and pathological features in human tissues.
AI Creates New ‘Alarm Bells’ for Prostate Cancer Recurrence
A study by Human Technopole, the Institute of Cancer Research and the Royal Marsden NHS Foundation Trust in London has shown that in prostate cancer the presence in the same tumour of cells with large differences in shape and genetic composition indicates an increased risk of relapse, including after a decade. The study may help doctors better tailor treatment for this disease, adopting more aggressive therapies in cases where these parameters indicate a higher risk of disease recurrence.
The National Facility for Genome Engineering & Disease Modelling with Giovanni Fagà
Meet Giovanni Fagà, Head of the National Facility for Genome Engineering & Disease Modelling. The core mission of the National Facility for Genome Engineering and Disease Modelling is to provide access to cutting-edge technologies in pluripotent stem cells, cell model generation, and genomic engineering.
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