Moritz Gerstung
Scientist developing AI for oncology. Division head at the German Cancer Research Centre DKFZ. Prof at the University of Heidelberg, Germany. Previously at EMBL-EBI and Wellcome Sanger Institute. Alumnus of ETH Zurich.
- Reposted by Moritz Gerstung🚨 new glioblastoma preprint alert! we present the first spatially resolved single cell atlas comparing radionecrotic changes (RN) and recurrent IDH-wildtype glioblastoma (GB) –– shedding light on a long-standing diagnostic challenge. 🧵 1/
- Join us: With @abigailsuwala.bsky.social we are looking for a postdoc to drive our spatial transcriptomics analysis efforts of brain tumours. If you enjoy multidisciplinary biomedical research, big data and coding, you are in the right place. karriere.klinikum.uni-heidelberg.de/index.php?ac...
- A spatial transcriptomics analysis led by my student Zaira reveals the distinct nature (gene expression, tumour cell states and local microenvironments) of radionecrosis and recurrence in glioblastoma. 👏
- Reposted by Moritz GerstungScientists from @embl.org and DKFZ have developed an AI model that assesses the long-term individual risk for more than 1,000 diseases. The model can predict health events over a period of more than a decade. @moritzgerstung.bsky.social @nature.com t1p.de/zmjfg
- Reposted by Moritz GerstungWhat if you could get a glimpse of the future of your health, today? Our scientists have developed a new generative AI model, trained using large-scale health records, that can estimate how human health may change over time. Watch to find out more. 🖥️🧬
- This is a great set of methods for studying combinatorial effects of cancer mutations on spatial phenotypes. Clever experimental design by @breinigmarco.bsky.social hijacking Visium and elegant analysis by @lomakinai.bsky.social and @elihei.bsky.social www.nature.com/articles/s41...
- Reposted by Moritz GerstungHow does tumour heterogeneity arise? How can we predict cancer cell plasticity? In 2 new studies, we trace #glioblastoma heterogeneity to a spatial cancer cell trajectory w. multimodal cell atlassing bit.ly/4mkrWgs & predict plasticity w. snRNA/ATAC+deep learning bit.ly/3FbI6Ic 🧵
- Reposted by Moritz GerstungSpatial biology of cancer evolution go.nature.com/44hbib6 #Review by Zaira Seferbekova, @lomakinai.bsky.social, Lucy R. Yates & @moritzgerstung.bsky.social Free to read here: rdcu.be/c1jwe
- Some classical statistics today. ebmstate, an R package for multistate models with empirical Bayes covariate effect estimation. Developed by Rui Costa during his postdoc in my group. doi.org/10.32614/RJ-...
- This type of model predicts a patient’s journey across several mid- and endpoints and relates the progression to hundreds of variables. It was used to learn detailed prognostic models for acute myeloid leukaemia .. www.nature.com/articles/ng....
- and also myeloproliferative neoplasms. Back then, the implementation was very clunky and could only be done by R experts. ebmstate now makes the inference much easier with only a few lines of code. www.nejm.org/doi/full/10....
- Reposted by Moritz GerstungFrom great collaborations come great things. Excited to share Segger, the solution to segmentation of spatial transcriptomics (ST) data, with the @steglelab.bsky.social and @moritzgerstung.bsky.social labs, spearheaded by the great Andrew Moorman and Elyas Heidari www.biorxiv.org/content/10.1...
- Reposted by Moritz Gerstung1/ New preprint! 🍳 @elihei.bsky.social and our team at @embl.org , @dkfz.bsky.social, and @mskcancercenter.bsky.social built #segger - a fast, accurate cell segmentation tool for spatial transcriptomics that assigns transcripts to their cell origins! doi.org/10.1101/2025...
- Spatial transcriptomics holds great promise to understand biological tissue function, but the assignment of transcripts to cells has been a substantial bottleneck. For this reason, Elyas Heidari, a student in my lab and in @steglelab.bsky.social built segger. www.biorxiv.org/content/10.1...
- Segger is a super fast graph neural network algorithm, which makes cell segmentation much more reliable and faster.
- Andrew Moorman and other members of @danapeer.bsky.social's lab helped carry out a rigorous assessment based on various 10x Xenium data sets with bespoke segmentation stainings, providing the ground truth to demonstrate segger's superior performance.
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View full threadAlso tagging Elyas Heidari @elihei.bsky.social here who led this fantastic work.
- Reposted by Moritz GerstungNew preprint! We worked with @msftresearch.bsky.social and @broadinstitute.org to see whether large language models (LLMs) can be useful to variant scientists in deciding whether genetic variants seen in a patient are responsible for their disease. tl;dr yes they can: www.biorxiv.org/content/10.1...
- Reposted by Moritz GerstungLooking forward to @moritzgerstung.bsky.social seminar @uob-ieu.bsky.social on 30th Jan 2025 at 1pm, ,“ Using AI to Predict Disease Risks” in person and on line bristol-ac-uk.zoom.us/j/94273829130
- Reposted by Moritz GerstungResharing here a recent X post. In this preprint, we introduce an improved version of NanoSeq, a duplex sequencing protocol with <5 errors per billion bp in single DNA molecules, and use it to study the somatic mutation landscape of oral epithelium in >1000 people. 1/ www.medrxiv.org/content/10.1...
- Reposted by Moritz GerstungWe strongly suggest that academic publishers and other platforms that host research rapidly implement a Share to Bluesky button for their articles. Here's how: docs.bsky.app/docs/advance... #AcademicSky #HigherEd #Altmetrics
- 5:20 start this morning for a virtual talk at the WEHI in Melbourne, covering: * Cancer risk models * Delphi multi-disease genAI * Paion, a new brain tumor digital pathology algorithm Summary below:
- 1/3 Cancer risk models based on national health registries * trained 6.7M individuals from DK * validate in UK * disease history most important predictor for age >65 * family history predicts early onset cancers www.thelancet.com/journals/lan...
- 2/3 Delphi-2M generative AI model for multi-disease risks * a single GPT-derived model learns risks of 1257 diseases * samples future life courses * offers insights into multi-morbidity dynamics www.medrxiv.org/content/10.1...
- 3/3 Paion is a new algorithm to diagnose brain cancers from H&E images. * distinguishes 102 types of brain cancer * high confidence predictions in 50-70% of cases across 7 cohorts * top1 acc > 85% in high conf subset * 2 day turnaround v 3 weeks for molecular testing Preprint coming soon.
- Reposted by Moritz GerstungNew at Science Evo, a large language of life (LLLM) genomic foundation model, predicting & generating tasks from molecular to genomic scale science.org/doi/10.1126/...
