Public Notice for HT’s list of lawyers online
The public notice for the creation of a list of lawyers, from which legal representation assignments may be drawn in the interest of the Human Technopole Foundation, is now online.
Interested parties can review the documentation here.
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Expanding the reach of the pangenome era with COSIGT
Researchers at Human Technopole have developed a novel pangenome-based pipeline that captures structural genomic diversity from low-coverage sequencing data at the population level. Their innovative work, including a step-by-step protocol, is published in Genome Biology.
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UniMi and Human Technopole strengthen collaboration on research and education
Scientific research in the life sciences, materials science, biomedical and environmental fields, alongside joint education programmes and reciprocal access to laboratories and infrastructure, are at the heart of the six-year agreement through which the two institutions are strengthening their cooperation as key players in MIND – Milano Innovation District.
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HT Management Committee – Call for Candidates
Human Technopole, a leading biomedical and life sciences research institute based in Milan, is seeking three highly qualified candidates to join its Management Committee, the governing body responsible for the operational leadership of the Foundation.
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Beyond cancer dependency maps: the PRECISE vision
PRECISE (Predictive Relationships Explaining Cancer Genetic Interactions and Synthetic Essentiality) is a pan-European initiative that aims to move cancer research beyond cataloguing cancer vulnerabilities towards predicting them before they are experimentally tested. Human Technopole (HT) is among the founding partners of the consortium, with Francesco Iorio leading its scientific activities across the institute alongside other HT research groups. The consortium and its scientific vision are presented in a Nature Genetics commentary.
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When less is more: optimising fluorescence microscopy with Micro𝕊plit
Researchers at Human Technopole have developed a novel machine learning-based method that transforms composite fluorescence microscopy images with overlapping signals into separate images revealing individual cellular structures. The tool is published in Nature Methods, with experimental models and training data openly available on GitHub.