Moritz Gerstung
@moritzgerstung.bsky.social
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.
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-...
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-...
March 20, 2025 at 2:03 PM
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-...
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-...
A great thanks goes to all the other authors and contributors from the Gerstung, Stegle and Peer labs who made this work possible.
March 17, 2025 at 4:19 PM
A great thanks goes to all the other authors and contributors from the Gerstung, Stegle and Peer labs who made this work possible.
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.
March 17, 2025 at 4:19 PM
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.
Segger is a super fast graph neural network algorithm, which makes cell segmentation much more reliable and faster.
March 17, 2025 at 4:19 PM
Segger is a super fast graph neural network algorithm, which makes cell segmentation much more reliable and faster.
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...
For this reason, Elyas Heidari, a student in my lab and in @steglelab.bsky.social built segger.
www.biorxiv.org/content/10.1...
March 17, 2025 at 4:19 PM
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...
For this reason, Elyas Heidari, a student in my lab and in @steglelab.bsky.social built segger.
www.biorxiv.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.
* 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.
November 18, 2024 at 8:20 AM
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.
* 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.
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...
* 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...
November 18, 2024 at 8:20 AM
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...
* 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...
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:
* Cancer risk models
* Delphi multi-disease genAI
* Paion, a new brain tumor digital pathology algorithm
Summary below:
November 18, 2024 at 8:20 AM
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:
* Cancer risk models
* Delphi multi-disease genAI
* Paion, a new brain tumor digital pathology algorithm
Summary below: