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DTSTART;TZID=Australia/Sydney:20261026T130000
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SUMMARY:Foundation Models for Genomics: From Epigenomic Prediction to Biological Reasoning
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Wenpin Hou\, Duke University \n\n\n\nThis is an online event held via Zoom.Zoom link: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nRecent advances in foundation models and large language models (LLMs) are creating new opportunities for analyzing complex epigenomic data and accelerating biomedical discovery. However\, important challenges remain in learning meaningful biological representations and ensuring that model predictions and reasoning are accurate\, reliable\, and biologically grounded. In this talk\, I will present our research in two complementary directions. First\, I will discuss the development of foundation models for genomic and epigenomic data\, highlighting how deep learning can capture complex relationships between genomic context and DNA methylation to enable genome-wide epigenomic prediction without requiring direct methylation measurements. Second\, I will discuss our efforts to apply and systematically evaluate LLMs for biological reasoning and computational analysis\, including cell-type annotation\, biological knowledge assessment\, and biomedical image classification. I will highlight both the capabilities and limitations of current models\, as well as approaches to improve their reliability through biological knowledge and ontology grounding. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Wenpin Hou\n\n\n\nDr Wenpin Hou is an Assistant Professor in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine\, affiliated with New York Genome Center. Her research lies at the intersection of computational biology\, artificial intelligence\, and statistical genomics\, with a focus on developing computational methods to understand gene regulation using single-cell\, spatial\, and epigenomic data. More recently\, her group has focused on foundation models and AI-driven approaches for genomics\, aiming to build predictive models of cellular states and regulatory programs across diverse biological contexts. Prior to joining Duke\, Dr Hou was an Assistant Professor in the Department of Biostatistics at Columbia University and an affiliated member of the Data Science Institute. She completed her postdoctoral training in Biostatistics and Computer Science at Johns Hopkins University and received her Ph.D. in Mathematics from The University of Hong Kong. Her work has been recognized through NIH Pathway to Independence Award (K99/R00) and Maximizing Investigators’ Research Award (MIRA) for Early Stage Investigators. She is also a recipient of the 2025 Mentor of the Year Award at Columbia University Irving Medical Center.
URL:https://spds.sydney.edu.au/event/foundation-models-for-genomics-from-epigenomic-prediction-to-biological-reasoning/
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DTSTART;TZID=Australia/Sydney:20261109T130000
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DTSTAMP:20261021T003844Z
CREATED:20261009T035522Z
LAST-MODIFIED:20261021T003844Z
UID:5561-1794229200-1794232800@spds.sydney.edu.au
SUMMARY:Large-scale GWAS integrated with fine-mapping and single-cell analyses prioritizes effector genes\, cell-contexts of glaucoma risk
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Han Wang\, Peking University \n\n\n\nThis is an online event held via Zoom.Zoom link: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nPrimary open-angle glaucoma (POAG) is the leading cause of irreversible blindness worldwide\, and the genetic architecture of POAG at cellular level remains incompletely understood. In this study\, we conducted a large-scale genome-wide association study of POAG involving approximately 3 million individuals (97\,229 cases) and identified 495 loci (198 novel). External evaluation in 23andMe Research Institute (114\,468 cases and 3\,525\,660 controls) showed that 412 (84.6%) had P < 0.0001 after Bonferroni correction. Integrating multi-omics datasets with advanced fine-mapping approaches\, we developed a glaucoma gene prioritization framework and identified 285 effector genes. Single-cell RNA-seq analysis revealed retinal cell types including astrocytes\, Müller glia\, retinal pigment epithelial cells\, and retinal ganglion cells. Using our new scRNA-seq in primary trabecular meshwork cells (TMCs)\, we further prioritized TMC-context putative genes\, including ADAM15 and GAS7. Finally\, drug-repurposing analyses identified therapeutic target hypotheses for POAG. Together\, these findings provide a comprehensive genetic architecture for POAG\, highlighting novel effector genes and cellular contexts\, and offering potential drug repurposing opportunities. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHan Wang\n\n\n\nHan Wang is a PhD student (work with Dr Xikun Han) in Epidemiology and Biostatistics at Peking University. His research lies at the intersection of statistical genetics and ophthalmology genetics\, focusing on dissecting the genetic architecture of glaucoma and retinal diseases. He integrates large-scale GWAS\, single-cell RNA/ATAC sequencing\, and gene prioritization to identify effector genes and to repurpose existing drugs for glaucoma.
URL:https://spds.sydney.edu.au/event/predicting-cellular-perturbations-biological-benchmarks-and-ai-assisted-model-discovery/
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