Sydney Precision Data Science Centre

TenK10K multiome initiative: Genetic regulation of cell-type–specific chromatin accessibility shapes immune function and disease risk
2025-08-11 13:00 - 14:00 (Australia/Sydney)
Statistical Bioinformatics SeminarDr Angli Xue, Garvan Institute of Medical Research

This was an online event held via Zoom.

Understanding how genetic variation influences gene regulation at the single-cell level is crucial for elucidating the mechanisms underlying complex diseases. However, limited large-scale single-cell multi-omics data have constrained our understanding of the regulatory pathways that link variants to gene expression. Here, we present the TenK10K multiome initiative as part of the TenK10K phase 1 projects: chromatin accessibility profiles from 3.5 million peripheral blood mononuclear cells (PBMCs) across 1,013 donors, generated using single-cell ATAC-seq and multiome (RNA+ATAC) sequencing, with matched whole-genome sequencing. We characterised ~441,000 chromatin peaks across 28 immune cell types and mapped ~243,000 chromatin accessibility QTLs (caQTLs), 60% of which are cell-type-specific. Integration with TenK10K scRNA-seq (5.4 million PBMCs) identified 31,688 candidate cis-regulatory elements colocalised with eQTLs; over half (52.5%) show evidence of causal effects mediated via chromatin accessibility. Combining caQTLs with GWAS loci for 17 diseases and 44 blood traits uncovered 10–41% more colocalised signals compared to eQTLs alone. For example, incorporating caQTLs increased the number of candidate inflammatory bowel disease (IBD) genes in CD8 effector memory T cells from 39 to 55. We demonstrate cell-type-specific mechanisms, such as a regulatory effect on IRGM acting through altered chromatin accessibility in CD8 effector memory T cells but not in naïve cells. Using a graphical neural network, we link peaks to genes in unpaired multiome data with up to 80% higher accuracy than with paired data lacking QTLs, improving gene regulatory network inference by identifying 128 additional TF–target pairs (a 22% increase). These findings provide an unprecedented single-cell map of chromatin accessibility and genetic variation in human circulating immune cells, establishing a powerful resource for dissecting cell-type-specific regulation and advancing our understanding of genetic risk for complex diseases.

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Dr Angli Xue

Dr Angli Xue is an NHMRC Investigator Fellow (EL1) and a postdoctoral researcher with Prof Joseph Powell at the Garvan Institute of Medical Research. He currently leads the multiome stream of the TenK10K project — a population cohort study aiming to map 50 million human cells from 10,000 individuals. His research focuses on leveraging large-scale multi-omics data to uncover cell type–specific genetic regulatory mechanisms and identify novel drug targets. Dr Xue earned his BSc from Zhejiang University and completed a PhD in Statistical Genetics at The University of Queensland under the supervision of Prof Jian Yang. He is the recipient of an NHMRC Investigator Grant (2025–2029) and a Ramaciotti Health Investment Grant as Chief Investigator.

The lazy way of pathological images analysis in the era of large models
2025-08-18 13:00 - 14:00
Statistical Bioinformatics SeminarDr Yimin Zheng, CeMM Research Center for Molecular Medicine

This was an online event held via Zoom.

Histopathological slides remain the gold standard for disease diagnosis and the study of tissue architecture and pathology, offering an exceptionally rich yet often underutilized source of biomedical information. Unlike single-cell datasets, pathology slides are readily available in clinical workflows and comparatively inexpensive to generate.

Recent advances in foundation models have transformed our ability to extract meaningful insights from image data, leading to a surge of high-impact studies leveraging pathology images for survival prediction, subtyping, spatial integration, and more. However, for many bioinformaticians and systems biologists, the field of image analysis still feels inaccessible because of steep technical barriers: diverse file formats, fragmented tools, and a lack of intuitive frameworks for exploration and analysis.

Is there a way to analyze pathology images as intuitively as we analyze single-cell data? The answer is yes!

In this talk, I will introduce LazySlide, an open-source platform for pathology image analysis specifically designed for users scientists already comfortable with tools like Scanpy and AnnData. LazySlide supports multiple image formats and enables interaction with large models. This talk will demonstrate how LazySlide empowers scientists to “analyze images with text” and incorporate rich histopathological information into multi-omics workflows, without needing to become image processing experts. Whether you’re studying cancer progression, tissue aging, or immune landscapes, LazySlide unlocks a new dimension of spatial and morphological context at scale.

Get started with LazySlide in no time: https://github.com/rendeirolab/LazySlide

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Dr Yimin Zheng

Dr Yimin Zheng is Postdoctoral Fellow at CeMM Research Center for Molecular Medicine at Vienna, Austria. He comes from a spatial biology background. His current research focuses on understanding cancer metastasis and aging from a multimodel pathological imaging perspective. He is also an open source contributor to multiple scientific software projects and a big-fan on visualization who has developed the composable visualization package of Marsilea.

The secret sauce - building insights from integrated atlas of human biology
2025-08-25 13:00 - 14:00 | Mackenzie Room, Level 6, Charles Perkins Centre, University of Sydney, Johns Hopkins Drive, University of Sydney, Sydney, 2006, Australia
Judith and David Coffey SeminarProf Christine Wells, University of Melbourne

This event was held in-person and online.

The human cell atlas is a global consortium seeking to build a detailed map of cell types through developmental time, health and disease states. Molecular snapshots of individual cells can be taken at many measurement levels, and the expectation is that integrating multiple modalities will lead to new discoveries about human cell development and behaviour. Data integration at this scale requires two main considerations – the first is statistical models to remove experimental artefacts and harmonise informative signals ; the second is curation of the metadata to identify possible technical, experimental or even biological confounders. We often pay a lot of attention to the first challenge, at the expense of minimizing or ignoring the second, simply because data curation is difficult, and often requires biological expertise that sits outside of our own groups.

In this talk, I’ll introduce generative AI driven tools developed by my own team for stem cell curation, as well as provide a preview of the community-driven curation platforms being lead out of the Human Cell Atlas consortium and provide some examples of how these tools are supporting high quality data integration and the creation of new cell atlases.

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Prof Christine Wells

Professor Christine Wells is chair of stem cell systems at the University of Melbourne. She uses computational models to understand human stem cell behaviour, and pluripotent stem cell models to test these computational predictions. Christine graduated with a PhD from UQ in 2004, and established her first laboratory at Griffith University in 2005. Over the past 20 years, her team has built several human cell atlases describing immune cell subsets, then uses these atlases to benchmark lab grown immune cells, and have invented new protocols to make specialist immune cells in the laboratory. Christine has authored over 150 papers, collectively cited >30,000 times. She is a member of several international consortia including the Functional Annotation of the Mammalian genome (FANTOM), the Human Cell Atlas (HCA) and the equity working group for the International Society for Stem Cell Research. Christine is the architect of the Stemformatics data collaboration platform and academic lead of the Australian stem cell registry.

A Systematic Comparison of Single-Cell Perturbation Response Prediction Models
2025-09-01 13:00 - 14:00
Statistical Bioinformatics SeminarDr Yue You, Guangzhou National Laboratory

This was an online event held via Zoom.

Predicting single-cell transcriptional responses to perturbations is central to dissecting gene regulation and accelerating therapeutic design, yet the field lacks a rigorous, task-spanning assessment of model behavior. We present a large-scale benchmark of 12 representative methods and 3 baselines across 25 datasets spanning diverse perturbation modalities and species, including two new primary immune-cell drug-response resources. We evaluated three core tasks—generalization to unseen single-gene perturbations, prediction of combinatorial interactions, and transfer across cell types—using 24 metrics covering expression-level accuracy, relative changes, differential expression recovery, and distributional similarity.

Across tasks, performance depended strongly on perturbation effect size and evaluation perspective: expression-level agreement was highest for small-effect perturbations resembling controls, whereas delta- and DE-based metrics improved with larger effects, providing clearer signals. Models shared a conservative bias, with fine-tuned foundation models compressing variance and underestimating synergistic effects in combinations. PerturbNet showed superior recovery of DE signatures in Tasks 1 and 2, while no method consistently generalized across cell types in Task 3, where dataset heterogeneity dominated outcomes.

This benchmark establishes current methodological limits, clarifies when metrics diverge, and provides a foundation for developing virtual-cell models that more faithfully capture heterogeneous perturbation responses.

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Dr Yue You

Dr Yue You is a postdoctoral researcher at the Guangzhou National Laboratory. Her research centers on developing single-cell multi-omics algorithms and spatial-omics data analysis to decode cellular dynamics under genetic and pharmacological perturbations.

Large Models for Single-Cell Omics and Drug Discovery: Data, Pretraining, and Closed-Loop Environment
2025-09-08 13:00 - 14:00
Statistical Bioinformatics SeminarDr Haotian Cui, University of Toronto

This was an online event held via Zoom.

This presentation will discuss recent advances in foundation models for single-cell omics and therapeutic discovery, focusing on the development of scGPT, a generative transformer model trained on over 33 million single-cell profiles. It will cover key design principles—such as generative pretraining and multi-task alignment—that enable broad applications including cell type annotation, perturbation response prediction, and reference mapping. The talk will also highlight emerging multimodal extensions such as scGPT-spatial and MethylGPT, and emphasize the importance of perturbational training for modeling cellular dynamics. Finally, it will introduce LUMI-lab, a closed-loop self-driving platform developed by Dr. Cui and collaborators, which integrates model-guided design, synthesis, and validation to accelerate mRNA delivery—demonstrating a scalable framework for virtual cell modeling and active learning in biology.

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Dr Haotian Cui

Dr. Haotian Cui is a researcher specializing in machine learning, genomics, and drug discovery. He recently completed his Ph.D. in Computer Science at the University of Toronto, advised by Prof. Bo Wang. His research focuses on developing large-scale self-supervised and generative foundation models for single-cell omics and molecular biology, integrating AI with experimental pipelines to accelerate therapeutic discovery. He is the lead author of scGPT—one of the first generative foundation models for single-cell multi-omics. He also led the development of LUMI-lab, an autonomous AI-driven platform for mRNA therapeutics. He has published numerous papers in leading journals and conferences, including Nature, Nature Methods, Nature Communications, ACL, EMNLP, ICML.

Bifidobacteria support optimal infant vaccine responses
2025-09-15 13:00 - 14:00
Statistical Bioinformatics SeminarDr Feargal J. Ryan, Flinders University

This was an online event held via Zoom.

Accumulating evidence indicates that antibiotic exposure may lead to impaired vaccine responses; however, the mechanisms underlying this association remain poorly understood. Here we prospectively followed 191 healthy, vaginally born, term infants from birth to 15 months, using a systems vaccinology approach to assess the effects of antibiotic exposure on immune responses to vaccination. Exposure to direct neonatal but not intrapartum antibiotics was associated with significantly lower antibody titres against various polysaccharides in the 13-valent pneumococcal conjugate vaccine and the Haemophilus influenzae type b polyribosylribitol phosphate and diphtheria toxoid antigens in the combined 6-in-1 Infanrix Hexa vaccine at 7 months of age. Blood from infants exposed to neonatal antibiotics had an inflammatory transcriptional profile before vaccination; in addition, faecal metagenomics showed reduced abundance of Bifidobacterium species in these infants at the time of vaccination, which was correlated with reduced vaccine antibody titres 6 months later. In preclinical models, responses to the 13-valent pneumococcal conjugate vaccine were strongly dependent on an intact microbiota but could be restored in germ-free mice by administering a consortium of Bifidobacterium species or a probiotic already widely used in neonatal units. Our data suggest that microbiota-targeted interventions could mitigate the detrimental effects of early-life antibiotics on vaccine immunogenicity.

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Dr Feargal J. Ryan

Dr Feargal J. Ryan is an NHMRC Investigator and head of the Computational Multi-Omics Group at Flinders University. He specializes in systems biology, bioinformatics and the use of multi-omics data to study molecular mechanisms of health and disease in humans. Dr Ryan has co-authored over 45 peer reviewed papers including in top journals such as Nature and Science, which have spanned the human microbiome, idiopathic diseases, infection and cancer. He is also an advocate for supporting bioinformatics research in Australia and last year was elected as Vice-President of the national Australian Bioinformatics and Computational Biology Society (ABACBS).

Cell Simulation as Cell Segmentation
2025-09-22 13:00 - 14:00
Statistical Bioinformatics SeminarDr Daniel Jones, Fred Hutchinson Cancer Institute

This was an online event held via Zoom.

Spatial transcriptomics has grown rapidly in scale and adoption, but the quality and interpretability of the data is marred by the limited ability of prior cell segmentation methods to accurately assign transcripts to cells. Segmentation error causes systematic misidentification of cell types and profoundly confounds many spatial analyses. To help address this issue, we developed a probabilistic segmentation algorithm, Proseg, which uses the spatial distribution of transcripts to determine plausibly boundaries, dramatically reducing the degree of spurious coexpression downstream false positive results. Recent updates to the method adds support for Visium HD, handling both imaging- and barcode-based transcriptomic data with the same unified segmentation framework.

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Dr Daniel Jones

Daniel Jones is a staff scientist at the Fred Hutchinson Cancer Institute in the lab of Evan Newell since 2020 after receiving his PhD in Computer Science and Engineering at the University of Washington. His work focuses on probabilistic modeling and inference in spatial transcriptomics.

Open-ST: High-resolution spatial transcriptomics in 2D and 3D
2025-09-29 13:00 - 14:00
Statistical Bioinformatics SeminarDaniel León-Periñán, Berlin Institute for Medical Systems BiologyDr Elena Splendiani, Sapienza University of Rome

This was an online event held via Zoom.

Spatial transcriptomics (ST) has significantly advanced our understanding of the molecular mechanisms involved in tissue development, homeostasis, and disease. However, there is a need for easy-to-use, high-resolution and cost-efficient methods that can be scaled up for the analysis of tissues in 3D. To address these challenges, we introduce Open-ST, a high-resolution, sequencing-based platform, designed for the analysis of tissue molecular organization in both 2D and 3D. This experimental and computational resource is open-source, modular and cost-effective, making it accessible to a broad range of researchers and facilitates the adaptation of new implementations.

Open-ST has proven effective in various contexts. In mouse brain tissue, it captured transcripts at subcellular resolution and successfully reconstructed cell types. In a primary head-and-neck tumor and patient-matched healthy and metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations, findings that were corroborated by imaging-based ST. Notably, distinct cellular states were organized around cell-cell communication hotspots in the primary tumor. These transcriptomic states were maintained in the metastasis, despite the spatial organization being disrupted. Reconstructing the metastatic lymph node into a “3D virtual tissue block” from serial tissue sections, identified spatially contiguous structures that were not discernible in 2D. These included potential biomarkers located at the 3D tumor/lymph node boundary.

Currently, we are extending Open-ST to increase its applicability to a wider range of research questions, from small RNAs to long-reads, as well as extending its application to formalin-fixed paraffin-embedded tissues. Furthermore, integrating immunofluorescence staining in the workflow allows a multiomic perspective on the tissue architecture. Given its accessibility and versatility, Open-ST can be adopted and customized by a diverse range of users, enabling its application to increasingly specialized studies.

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Daniel León-Periñán

Dani is a computational biologist pursuing his PhD in the Rajewsky lab at the Berlin Institute for Medical Systems Biology (BIMSB-MDC). His research focuses on understanding how gene expression patterns, in space and time, can predict disease progression, by developing methods that generate, analyze and integrate large-scale data. With a background in Biotechnology and Computer Science, he works at the intersection of spatial biology, data visualization, and machine learning, with the aim to make biological data accessible and searchable.

Dr Elena Splendiani

Elena is a postdoctoral research fellow at the Department of Experimental Medicine, Sapienza University of Rome. She received her PhD in Molecular Medicine from Sapienza University in 2023 and her Master’s degree in Genetics and Molecular Biology in 2019 from the same institution. Her research focuses on solid tumors, with a particular interest in biomarker discovery and RNA-level molecular characterization, including both coding and non-coding RNAs. She co-developed a high-spatial-resolution technology, Open-ST, which she is currently applying to characterize tumors and study their microenvironments.

Using genomics-informed agent-based models to understand neoplastic phenotype transitions in the human PDAC microenvironment
2025-10-13 13:00 - 14:00
Statistical Bioinformatics SeminarDr Jeanette Johnson, University of Maryland

This is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391

We present a novel integration of data-driven single-cell analysis with mechanistic mathematical modeling to predict the impact of cancer-associated fibroblasts (CAF) on pancreatic ductal adenocarcinoma (PDAC) invasion. Bioinformatic analyses of high-throughput single-cell and spatial molecular assays give us access to human tumor gene expression at the time of measurement, enabling direct characterization of the human tumor microenvironment (TME), complementing our understanding of cell behavior gained from preclinical models. Notably, applying our Bayesian non-negative matrix factorization algorithm CoGAPS to single-cell RNA-seq data from PDAC allowed us to associate a gene program of concurrent epithelial-to-mesenchymal transition (EMT) and inflammation in epithelial cells with the presence of CAFs, validated through organoid co-culture experiments. We hypothesize that this gene program represents a mechanism by which CAFs promote invasive behavior in neoplastic cells over time. Testing this hypothesis requires inference of temporal changes, which is not possible with single-timepoint data collection in genomics data. In contrast to data-driven bioinformatics, mechanistic mathematical modeling can capture processes that happen over time and make predictions about a system. Agent-based models (ABMs) are one form of such mechanistic mathematical models that are well suited for temporal modeling of cellular phenotypes. We developed new software for ABMs that abstract cells into software agents with individual states and their own rules of behavior from single-cell and spatial molecular assays for temporal inference. We apply this technique to understand how CAFs in the PDAC microenvironment support the series of phenotype transitions comprising tumor progression and invasion, by encoding the CAF-induced ability of tumor cells to migrate and a cessation of proliferation in tumor cells, induced by CAF-secreted, non-diffusable substrate as inferred from our genomics analysis. While this provides a virtual framework to test the impact of varying CAF density on tumor cell invasion, it initially lacked integration with the spatial architecture of human tumors. We then apply the ABM to human PDAC spatial transcriptomics data to forecast tumor behavior over time in a spatially-informed setting, varying the hypothesized mechanisms. This revealed that a substrate-driven mechanism results in a broad mesenchymal-like zone surrounding epithelial-like outgrowths, while a cell contact-mediated mechanism results in a single-cell-wide boundary surrounding more significant outgrowth. When we compared these to real tumor compositions, this favors the explanation that secreted CAF substrate drives EMT in PDAC neoplastic cells, which we then validate in organoids with CAF conditioned media. Our work demonstrates how integrating ABMs with genomic and spatial data enables testing of mechanistic hypotheses about tumor-stroma interactions and provides deeper insights into PDAC microenvironments, potentially informing strategies to intercept disease progression.

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Dr Jeanette Johnson

I am currently a postdoctoral fellow at the University of Maryland School of Medicine, after completing my PhD in Immunology at Johns Hopkins this past May. My genomics obsession started as an undergraduate student at the University of British Columbia in computer science and immunology when I got to write processing scripts for some single-cell PBMC data. I will be presenting some of my PhD work which I did in Dr. Elana Fertig’s lab, looking at ways to build agent-based computational models systematically from genomics data, which I developed in the context of the pancreatic tumor microenvironment. Living in Baltimore, Maryland with my two cats and partner, my favorite hobby is convincing people to go eat seafood with me. I also love to do my nails and am actively searching for the maximally dramatic set that still lets me type and pipette.

High-resolution Characterization of Age-specific Changes in HPV-negative HNSCC through Building a scRNA-Sequencing Atlas
2025-10-20 13:00 - 14:00
Statistical Bioinformatics SeminarLina Kroehling, Boston University

This is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391

Age is strongly associated with both the incidence and mortality risk of head and neck cancer. While cancer and aging share many of their defining “hallmarks”, including chronic inflammation, increased genomic instability, and increased senescence, more research is needed to elucidate the specific mechanisms contributing to cancer aggressiveness in older patients.

To this end, we have assembled a hiqh-quality human single-cell RNA-sequencing HNSCC atlas profiling more than 290,000 cells across more than 70 patients, with ages ranging between 18 and 90, which provides a unique resource to investigate age-associated changes in the disease’s heterogeneity.

To create the atlas, we integrated seven publicly available single-cell RNAseq datasets from 73 HPV-negative patients. Cells were clustered, classified, and characterized by gene set enrichment analysis, both in the epithelial cell compartment and in the tumor microenvironment (TME). Differential expression and cell type proportion analyses were performed to identify genes and cell type compositional changes associated with age. Cell cell communication analysis was performed to identify interacting cell types and modeled to identify specific ligands and receptors changing with age.

We identified distinct age-related changes in cell type composition, including vascular endothelial cells increasing with age, and several tumor clusters with distinct functions also changing with age. Further analyses are ongoing, and we plan to functionally validate the hypotheses generated, specifically the presence of differentially abundant cell populations, and age-specific ligand-receptor signaling events that contribute to tumor growth.

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Lina Kroehling

Lina Kroehling is a bioinformatics PhD candidate in the Monti Lab at Boston University where she utilizes multi-omics data to study Head and Neck Cancer. She has a B.S. and M.S. in biochemistry from Clark University in Worcester MA, and worked as a bioinformatician in academic immunology labs before starting her PhD.

Harnessing the human microbiome for therapeutic purposes
2025-10-27 13:00 - 14:00
Statistical Bioinformatics SeminarDr Shanlin Ke, The Ohio State University

This is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391

The human microbiome plays a critical role in health and disease and represents a promising source for novel therapeutic strategies. In this talk, I will first provide an overview of how microbiomes are currently leveraged for therapeutic purposes. I will then introduce a computational approach we have developed to uncover complex host–microbiota interactions. Using our recent PTSD–microbiome study as an example, I will demonstrate how this approach can reveal host–microbiota interactions and be validated through animal models. Next, I will present our AI-based framework for discovering antimicrobial peptides (AMPs) from the urinary microbiome, highlighting its potential to combat antimicrobial resistance. I will conclude by discussing an idea: rejuvenating the human gut microbiome to preserve the “healthy” microbiome for future therapeutic use.

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Dr Shanlin Ke

Dr Shanlin Ke joined The Ohio State University as an Assistant Professor in May 2025 in the Division of Gastroenterology, Hepatology, and Nutrition within the Department of Internal Medicine. Dr Ke is a biologist with expertise in bioinformatics, microbiome, machine learning, multi-omics, and wet-lab techniques. He received his PhD in Animal Genetics from Jiangxi Agricultural University in 2020. In 2018, he joined Dr Yang-Yu Liu’s lab at the Channing Division of Network Medicine, Harvard Medical School as a visiting student. He continued his microbiome research as a postdoctoral fellow in Dr Liu’s lab beginning in 2021, where he developed and applied computational approaches to study host–microbiota interactions in chronic conditions (e.g., inflammatory bowel disease and post-traumatic stress disorder) and infectious diseases (e.g., Clostridioides difficile infection and urinary tract infection). In parallel, he conducted in vitro and in vivo experiments to validate computational findings and investigate the mechanisms underlying host-microbiota interactions in human diseases.

At OSU, Dr Ke’s lab focuses on developing innovative methodologies and leveraging bioinformatics tools, metagenomic sequencing, machine learning, and wet-lab techniques to investigate the role of the human microbiome in pancreatic diseases and to develop microbiome-based therapeutics.

Advancing Polygenic Risk Prediction: Integrating Information Across Populations and Health Records
2025-11-03 13:00 - 14:00
Statistical Bioinformatics SeminarLeqi Xu, Yale University

This is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391

Polygenic risk scores (PRS), defined as the weighted sum of risk alleles across a collection of genetic variants, have seen active development for predicting complex traits in recent years. PRS have demonstrated their ability to identify individuals with high disease risk, which can facilitate disease monitoring, prevention, and treatment.

Despite recent progress, two key challenges remain for PRS. First, most PRS have been developed in European populations, due to the dominance of large European cohorts in Genome-Wide Association Studies (GWAS). As a result, PRS derived for non-European populations often exhibit reduced accuracy. Second, GWAS phenotypes are often defined based on simplified criteria, typically grouping related ICD-10 diagnosis codes into binary disease definitions (case vs. control). This simplification potentially neglects the intricate phenotypic relationships embedded in detailed patient records, limiting both statistical power and clinical relevance.

In this talk, I will introduce two PRS frameworks developed to address these challenges. First, I present MIXPRS, an ensemble framework for multi-population PRS. MIXPRS integrates across methods and populations using only GWAS summary statistics, employing a data-fission strategy to generate pseudo-training and pseudo-tuning GWAS. By jointly analyzing diverse approaches, MIXPRS achieves robust and better prediction performance across settings.

Second, I present EEPRS, a framework that integrates trait-specific PRS with EHR-embedding–based PRS. We first extract phenotype embeddings from EHR data using methods such as Word2Vec and GPT, then perform GWAS on these embeddings to derive embedding-based PRS. By combining the GWAS results from embedding phenotypes with trait-specific PRS, EEPRS captures latent disease relationships and improves prediction accuracy.

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Leqi Xu

Leqi Xu is a PhD candidate in Biostatistics at Yale University, advised by Dr Hongyu Zhao. Her research focuses on developing statistical and computational methods for polygenic risk prediction and the integrative analysis of electronic health records and single-cell data.

Australian Data Science Network Conference 2025
2025-11-10 08:00 - 2025-11-12 05:00
Sydney Precision Data Science Centre is delighted to host the 4th Australian Data Science Network (ADSN) conference. The ADSN conference aims to connect Australia’s top experts in data science, fostering collaboration, expanding opportunities, and showcasing our collective capabilities.

Australian Data Science Network Conference 2025 Link

DETAILS

November 10-12

ORGANISERS

Charles Perkins Centre

University of Sydney

ORGANISERS

Australian Data Science Network

University of Sydney
