Grateful to my colleagues and to my supervisors, Lincoln Stein & Bo Wang, for their guidance and support. Stay tuned —more to come!
21.08.2025 19:53
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Grateful to my colleagues and to my supervisors, Lincoln Stein & Bo Wang, for their guidance and support. Stay tuned —more to come!
Thrilled to see my postdoctoral work published in @cellpress.bsky.social
OncoGAN generates simulated genomes to train genomic analysis tools —without the confidentiality risks of real genomes.
News story: t.co/J9QJZInPOE
Paper: t.co/ygEjM5vuGZ
#Genomics #Cancer #AI #Bioinformatics
🚀 Only 2 weeks left to join the VI Visualization Contest with R by Grupo de R de Asturias!
🏆 Prizes:
🥇 1st: 300€
🥈 2nd: 100€
Show off your #RStats skills and impress us with your best visualizations!
🔗 More info: github.com/grupoRasturi...
#Visualization #Contest #DataViz
8/8 More info:
Alongside the OncoGAN models and pipeline, we’ve released 800 synthetic genomes spanning 8 tumor types!
A huge thank you to all the authors for their contributions to this work!!!
📄 Preprint: tinyurl.com/yepheye3
📂 Datasets: tinyurl.com/28bpd5hs
💻 Code & Docs: tinyurl.com/mr3ku653
7/8 Is OncoGAN useful? Absolutely!
- We tested ActiveDriverWGS on synthetic genomes to see if it could detect the same driver genes as in real patient data, proving its value in refining algorithms and defining detection limits.
6/8 Is OncoGAN useful? Absolutely!
- We used OncoGAN simulations to augment DeepTumour’s training dataset (a tool for identifying tumor type based on somatic mutation patterns), showing performance improvements.
5/8 What does OncoGAN simulate?
- Copy number alterations (CNA) and structural variants (SV): This updated version successfully simulates CNAs and SVs.
4/8 What does OncoGAN simulate?
- Tumor heterogeneity (A): Simulating donors with varying mutational burdens and characteristics.
- Tissue-specific mutational patterns (B): Accurately modeling the genomic distribution of mutations and mutational signatures unique to different tumor types.
3/8 Why is OncoGAN necessary?
- Benchmarking: Since the ground truth of real cancer genomes is often unknown, evaluations typically compare methods, introducing potential bias. By generating open-access synthetic genomes with a known ground truth, OncoGAN helps improve and benchmark these tools.
2/8 Why is OncoGAN necessary?
- Improving data sharing: We have demonstrated that OncoGAN does not leak any private patient data from its training set, a crucial factor given the sensitivity of genetic information as protected health data.
Our updated version of OncoGAN is out! 🚀
🧬 OncoGAN is an AI system capable of generating high-fidelity, open-access synthetic cancer genomes.
Do you want to know more about it? 1/8 🦋
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We recently announced a dual new release of intOGen and boostDM
Computational analysis of 33,218 tumor genomes to identify cancer genes and driver mutations
➡️ Compendium of Cancer Driver Genes - www.intogen.org
➡️ In Silico Saturation Mutagenesis of Cancer Genes - www.intogen.org/boostdm