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X-WR-CALDESC:Events for Sydney Precision Data Science Centre
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DTSTART;TZID=Australia/Sydney:20250901T130000
DTEND;TZID=Australia/Sydney:20250901T140000
DTSTAMP:20260407T011715
CREATED:20250825T055007Z
LAST-MODIFIED:20250922T065653Z
UID:4142-1756731600-1756735200@spds.sydney.edu.au
SUMMARY:A Systematic Comparison of Single-Cell Perturbation Response Prediction Models
DESCRIPTION:Statistical Bioinformatics SeminarDr Yue You\, Guangzhou National Laboratory\n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nPredicting 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. \n\n\n\nAcross 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. \n\n\n\nThis 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. \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 Yue You\n\n\n\nDr 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.
URL:https://spds.sydney.edu.au/event/a-systematic-comparison-of-single-cell-perturbation-response-prediction-models/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250908T130000
DTEND;TZID=Australia/Sydney:20250908T140000
DTSTAMP:20260407T011715
CREATED:20250818T052708Z
LAST-MODIFIED:20250922T070418Z
UID:4113-1757336400-1757340000@spds.sydney.edu.au
SUMMARY:Large Models for Single-Cell Omics and Drug Discovery: Data\, Pretraining\, and Closed-Loop Environment
DESCRIPTION:Statistical Bioinformatics SeminarDr Haotian Cui\, University of Toronto\n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nThis 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. \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 Haotian Cui\n\n\n\nDr. 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.
URL:https://spds.sydney.edu.au/event/large-models-for-single-cell-omics-and-drug-discovery-data-pretraining-and-closed-loop-environment/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250915T130000
DTEND;TZID=Australia/Sydney:20250915T140000
DTSTAMP:20260407T011715
CREATED:20250718T043847Z
LAST-MODIFIED:20250922T070523Z
UID:3943-1757941200-1757944800@spds.sydney.edu.au
SUMMARY:Bifidobacteria support optimal infant vaccine responses
DESCRIPTION:Statistical Bioinformatics SeminarDr Feargal J. Ryan\, Flinders University\n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nAccumulating 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. \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 Feargal J. Ryan\n\n\n\nDr 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). 
URL:https://spds.sydney.edu.au/event/bifidobacteria-support-optimal-infant-vaccine-responses/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250922T130000
DTEND;TZID=Australia/Sydney:20250922T140000
DTSTAMP:20260407T011715
CREATED:20250905T052651Z
LAST-MODIFIED:20250922T070624Z
UID:4155-1758546000-1758549600@spds.sydney.edu.au
SUMMARY:Cell Simulation as Cell Segmentation
DESCRIPTION:Statistical Bioinformatics SeminarDr Daniel Jones\, Fred Hutchinson Cancer Institute\n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nSpatial 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. \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 Daniel Jones\n\n\n\nDaniel 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.
URL:https://spds.sydney.edu.au/event/cell-simulation-as-cell-segmentation/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250929T130000
DTEND;TZID=Australia/Sydney:20250929T140000
DTSTAMP:20260407T011715
CREATED:20250919T012153Z
LAST-MODIFIED:20251112T072543Z
UID:4181-1759150800-1759154400@spds.sydney.edu.au
SUMMARY:Open-ST: High-resolution spatial transcriptomics in 2D and 3D
DESCRIPTION:Statistical Bioinformatics SeminarDaniel León-Periñán\, Berlin Institute for Medical Systems BiologyDr Elena Splendiani\, Sapienza University of Rome\n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nSpatial 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. \n\n\n\nOpen-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. \n\n\n\nCurrently\, 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. \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\nDaniel León-Periñán\n\n\n\nDani 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. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Elena Splendiani\n\n\n\nElena 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.
URL:https://spds.sydney.edu.au/event/open-st-high-resolution-spatial-transcriptomics-in-2d-and-3d/
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