Category Archives: DBDS

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a photo of a sparkler in celebration
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Sylvia Plevritis’ five-year anniversary of leading DBDS

Congratulations to Sylvia on leading the Biomedical Data Science department for the past five years. With her strong leadership, direction, and guidance, DBDS is poised to become the leader in the AI/precision healthcare revolution. Sylvia has led the charge for DBDSand has reached milestones in research, discovery, increased grants, and growth with faculty and staff, proving itself to be one of the strongest departments in the School of Medicine. She is tireless, devoted and determined, working incessantly to propel our department to a position of eminence in the biomedical data science field. She is both a visionary and luminary; we could not ask for a more skilled pilot to chart the course for DBDS’ current and future success.

Olivier Gevaert and team have developed a biomedical model inspired by DALL-E, we use RNA expression profiles to generate synthetic digital pathology images across several cancer tissues. We show that these synthetic data can be used in combination with real data, cell type distributions are representative of real tissues and synthetic data can be used for self supervised learning. You can try the model here: https://lnkd.in/egWGGDYJ. We also generated 1M images for download: https://lnkd.in/eSsM9ZqA Amazing work by Francisco Carrillo Pérez in the lab, and only possible thanks to the Polaris compute resources and collaboration with Ravi Madduri at Argonne National Laboratory, U.S. Department of Energy (DOE). Full text is available here: https://rdcu.be/dBZJK. https://www.nature.com/articles/s41551-024-01193-8
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Olivier Gevaert: “Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models” published in Nature Biomedical Engineering

Olivier Gevaert and team have developed a biomedical model inspired by DALL-E, we use RNA expression profiles to generate synthetic digital pathology images across several cancer tissues.
You can try the model here: https://lnkd.in/egWGGDYJ. We also generated 1M images for download: https://lnkd.in/eSsM9ZqA
Amazing work by Francisco Carrillo Pérez in the lab, and only possible thanks to the Polaris compute resources and collaboration with Ravi Madduri at Argonne National Laboratory, U.S. Department of Energy (DOE).
Full text is available here: https://rdcu.be/dBZJK.
https://www.nature.com/articles/s41551-024-01193-8
Understanding the genetic factors that underlie the normal variation in cardiac anatomy is of great interest. In this study, Rodrigo Bonazzola et al. applied unsupervised geometric deep learning to phenotype the left ventricle using an MRI-derived three-dimensional mesh representation (as depicted on the cover). We show that this approach boosts genetic discovery and provides deeper insights into the genetic underpinnings of cardiac morphology. Check out https://lnkd.in/edrvTg2W
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Tanveer Syeda-Mahmood’s research featured in cover story for Nature Medicine Intelligence

Understanding the genetic factors that underlie the normal variation in cardiac anatomy is of great interest. In this study, Rodrigo Bonazzola et al. applied unsupervised geometric deep learning to phenotype the left ventricle using an MRI-derived three-dimensional mesh representation (as depicted on the cover). We show that this approach boosts genetic discovery and provides deeper insights into the genetic underpinnings of cardiac morphology.

Check out https://lnkd.in/edrvTg2W

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