AnderDN
@dn_ander
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I’m a computational biologist and biochemist 🧬 Postdoctoral researcher at University of Toronto and OICR
Joined September 2020
RT @FINBAsturias: IV Curso de Introducción a R, organizado por ISPA-FINBA y @grupoRasturias del 23 de septiembre al 25 de noviembre. Más in….
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RT @grupoRasturias: Un año más, desde el Grupo de R de Asturias, y en colaboración con @FINBAsturias, lanzamos el Curso de Introducción a R….
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RT @grupoRasturias: El 2º premio (100€) se lo ha llevado, también desde la Universidad de Granada:.👧Laura Jiménez.Os dejamos aquí unas cuan….
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RT @grupoRasturias: El 1er premio (300€) se lo han llevado, de la Universidad de Granada🥁🥁🥁:.👦Óscar Sobén (@oscarsoce12 ) 🤝Thalía Serrano (….
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🚀 Only 2 weeks left to join the VI Visualization Contest with R by @grupoRasturias . 🏆 Prizes:.🥇 1st: 300€.🥈 2nd: 100€. Show off your #RStats skills and impress us with your best visualizations!. 🔗 More info: #Visualization #Contest #DataViz
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RT @grupoRasturias: Bueno, bueno. pues aquí está uno de nuestros eventos más importantes del año. Nuestro "CONCURSO DE VISUALIZACIÓN DE….
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9/9 More info:. Alongside the OncoGAN models and pipeline, we’ve released 800 synthetic genomes spanning 8 tumor types!. 📄 Preprint: 📂 Datasets: 💻 Code & Docs:
biorxiv.org
Cancer originates from alterations in the genome, and understanding how these changes lead to disease is crucial for achieving the goals of precision oncology. Connecting genomic alterations to...
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8/9 A huge thank you to the rest of authors Xindi Zhang, Wei Jiao, @BoWang87 and Lincoln Stein for their contributions and guidance!!!. @ontariogenomics @CANSSIOntario @MoGen_Grad @OICR_news.
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7/9. - 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.
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6/9 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.
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5/9. - Copy number alterations (CNA) and structural variants (SV): This updated version successfully simulates CNAs and SVs.
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4/9 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.
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3/9. - Benchmarking: Since the ground truth of real cancer genomes is often unknown, evaluations typically compare methods, introducing potential bias. By generating synthetic genomes with a known ground truth, OncoGAN helps improve and benchmark these tools.
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2/9 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.
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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? 1/9
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RT @OpenlifesciAI: 🧬 Breaking Research: Omni-DNA.The team at @Microsoft and @ImperialCollege presents Omni-DNA: A Unified Genomic Foundatio….
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