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X-WR-CALDESC:Events for Sydney Precision Data Science Centre
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DTSTART;TZID=Australia/Sydney:20260504T130000
DTEND;TZID=Australia/Sydney:20260504T140000
DTSTAMP:20260601T155628
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SUMMARY:PantheonOS: An Evolvable Multi-Agent Framework for Automatic Genomics Discovery
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Weize Xu\, Stanford University \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nThe convergence of large language model-powered autonomous agent systems and single-cell biology promises a paradigm shift in biomedical discovery. However\, existing biological agent systems\, building upon single-agent architectures\, are narrowly specialized or overly general\, limiting applications to routine analyses. We introduce PantheonOS (https://PantheonOS.stanford.edu)\, an evolvable\, privacy-preserving multi-agent framework designed to reconcile generality with domain specificity. Critically\, PantheonOS enables agentic code evolution\, allowing evolving state-of-the-art batch correction and our reinforcement-learning augmented gene panel selection algorithms to achieve super-human performance. PantheonOS drives biological discoveries across systems: uncovering asymmetric paracrine Cer1–Nodal inhibition in proximal–distal axis formation of novel early mouse embryo 3D data; integrating human fetal heart multi-omics with whole-heart data to reveal molecular programs underpin heart diseases; and adaptively selecting virtual cell models to predict cardiac regulatory and perturbation effects. Together\, PantheonOS points towards a future where scientific discoveries are increasingly driven by self-evolving AI systems across biology and beyond. \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 Weize Xu\n\n\n\nDr. Weize Xu is a postdoctoral researcher in Dr. Xiaojie Qiu’s laboratory\, where he focuses on advancing computational biology and genomics research. He earned his Ph.D. in Dr. Gang Cao’s lab\, where he made significant contributions to the development of computational methods and pipelines for spatial transcriptomics (MiP-Seq) and single-cell Hi-C (sciDLO Hi-C). His work during this time centered on enhancing data analysis frameworks\, providing more precise insights into complex biological systems.Find out more on X:https://x.com/Nanguage
URL:https://spds.sydney.edu.au/event/pantheonos-an-evolvable-multi-agent-framework-for-automatic-genomics-discovery/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260511T130000
DTEND;TZID=Australia/Sydney:20260511T140000
DTSTAMP:20260601T155629
CREATED:20260424T020750Z
LAST-MODIFIED:20260515T012415Z
UID:4890-1778504400-1778508000@spds.sydney.edu.au
SUMMARY:Coupling Single-cell Genome & Epigenome to Study Functional Consequence of Somatic SVs
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Hyobin Jeong\, Yonsei University \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nSomatic structural variants are widespread in cancer\, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their functional consequences. We proposed a computational method\, scNOVA\, which utilizes Strand-seq to perform haplotype-aware integration of structural variant discovery and molecular phenotyping in single cells\, by using nucleosome occupancy to infer gene expression as a read-out. Application to leukemias and cell lines identifies local effects of copy-balanced rearrangements on gene deregulation\, and consequences of structural variants on aberrant signaling pathways in subclones. We discovered distinct SV subclones with dysregulated Wnt signaling in a chronic lymphocytic leukemia patient. We further uncovered the consequences of subclonal chromothripsis in T-cell acute lymphoblastic leukemia\, which revealed c-Myb activation\, enrichment of a primitive cell state and informed successful targeting of the subclone in cell culture\, using a Notch inhibitor. More recently\, scNOVA to complex karyotype acute myeloid leukemia (AML) revealed dynamic clonal evolution and targetable phenotypes. Not only cancer system\, we can apply this approach to study functional effect of somatic SVs in clonal hematopoiesis and aging. Also\, we are now extending scNOVA to develop scalable and broadly applicable bioinformatics methods which can link SVs to their functional effects\, to enable systematic single-cell multiomic studies of structural variation in heterogeneous cell populations. \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 Hyobin Jeong\n\n\n\nDr Hyobin Jeong is an assistant professor at Yonsei University in the Department of Systems Biology. She received her PhD in Interdisciplinary Bioscience and Biotechnology from Pohang University of Science and Technology (POSTECH). She has completed postdoctoral fellowships at the European Molecular Biology Laboratory (EMBL) and the Institute of Molecular Biology (IMB) in Germany\, and the Institute of Basic Science in Korea. Before her current role\, she was a Research Professor at the Hanyang Institute of Bioscience and Biotechnology.
URL:https://spds.sydney.edu.au/event/coupling-single-cell-genome-epigenome-to-study-functional-consequence-of-somatic-svs/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260518T130000
DTEND;TZID=Australia/Sydney:20260518T140000
DTSTAMP:20260601T155629
CREATED:20260424T022258Z
LAST-MODIFIED:20260522T023827Z
UID:4897-1779109200-1779112800@spds.sydney.edu.au
SUMMARY:Genetic regulation of human skeletal muscle: from bulk QTLs to single-nucleus resolution
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Wang Wenjing\, National University of Singapore \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nAlthough lifestyle-induced weight loss reduces type 2 diabetes risk\, how genetic variation shapes gene expression and splicing responses to weight loss remains poorly understood\, particularly in Asian populations. In the first part of this talk\, I will present our recently published work (Cell Genomics\, 2025) in which we profiled skeletal muscle transcriptomes from 54 overweight/obese Asian individuals before and after a 16-week lifestyle intervention in the Singapore Adult Metabolism Study (SAMS). The intervention resulted in ~10% weight loss and ~30% improvement in insulin-stimulated glucose uptake. We identified 505 differentially expressed genes enriched in mitochondrial function and insulin sensitivity pathways\, mapped cis-eQTLs and cis-sQTLs that are shared or condition-specific\, and integrated these regulatory variants with GWAS signals for metabolic traits to pinpoint candidate causal genes and mechanisms. In the second part\, I will introduce our ongoing effort to build a comprehensive single-nucleus and spatial transcriptomic atlas of human skeletal muscle. Using snRNA-seq from over 300 biopsies spanning lean and obese individuals across three Asian ancestries\, we have profiled more than one million nuclei and annotated over 20 cell types capturing substantial cellular heterogeneity across ancestry\, adiposity\, and metabolic states. We are now extending this resource with spatial transcriptomics (MERFISH and Xenium) on paired pre- and post-intervention samples to spatially resolve cell-type-specific regulatory programs and microenvironment remodeling during weight loss. Together\, these efforts establish the first longitudinal\, ancestry-diverse single-nucleus and spatial regulatory reference for Asian human skeletal muscle. \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\nWenjing Wang\n\n\n\nWenjing Wang is a third-year PhD candidate in the lab of Prof. Liu Boxiang at the National University of Singapore\, working at the intersection of functional genomics and metabolic disease. Her work combines bulk and single-nucleus RNA sequencing with QTL mapping to understand how genetic variation and lifestyle interventions reshape gene regulation across cell types. Her recent study on gene expression and splicing responses to exercise- and diet-induced weight loss was published in Cell Genomics (2025). She is currently building a large-scale single-cell and spatial atlas of human skeletal muscle across diverse Asian populations. \n\n\n\nFind out more on LinkedIn: https://www.linkedin.com/in/wenjing-wang-40a67b18b/
URL:https://spds.sydney.edu.au/event/genetic-regulation-of-human-skeletal-muscle-from-bulk-qtls-to-single-nucleus-resolution/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260525T130000
DTEND;TZID=Australia/Sydney:20260525T140000
DTSTAMP:20260601T155629
CREATED:20260518T023913Z
LAST-MODIFIED:20260529T064822Z
UID:5129-1779714000-1779717600@spds.sydney.edu.au
SUMMARY:Generalized cell phenotyping for spatial proteomics with language-informed vision models
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Xuefei (Julie) Wang\, California Institute of Technology \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nWe present DeepCell Types\, a novel approach to cell phenotyping for spatial proteomics that addresses the challenge of generalization across diverse datasets with varying marker panels collected across different platforms. Our approach utilizes a transformer with channel-wise attention to create a language-informed vision model; this model’s semantic understanding of the underlying marker panel enables it to learn from and adapt to heterogeneous datasets. Leveraging a curated\, diverse dataset named Expanded TissueNet with cell type labels spanning the literature and the NIH Human BioMolecular Atlas Program (HuBMAP) consortium\, our model demonstrates robust performance across various cell types\, tissues\, and imaging modalities. Comprehensive benchmarking shows superior accuracy and generalizability of our method compared to existing methods. This work significantly advances automated spatial proteomics analysis\, offering a generalizable and scalable solution for cell phenotyping that meets the demands of multiplexed imaging data. \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\nXuefei (Julie) Wang\n\n\n\nXuefei (Julie) Wang is a PhD student at Caltech co-advised by David Van Valen and Yisong Yue. Her research focuses on the fundamental principles of foundation models and agentic systems\, with a particular emphasis on spatial proteomics and transcriptomics. Julie’s work addresses the challenges of large-scale biological data through innovative modeling\, including the development of language-informed vision models for generalized cell phenotyping. Her goal is to build knowledge-centric agents that accelerate discovery by compounding insights from foundation tools and literature into a persistent knowledge base that grows more capable with use. In addition to her work at Caltech\, she has contributed to scientific AI efforts as a Student Researcher at Google Research with Michael P. Brenner.Connect with Xuefei:LinkedIn: xuefei-wangX: xuefei_whttps://xuefei-wang.github.io/
URL:https://spds.sydney.edu.au/event/generalized-cell-phenotyping-for-spatial-proteomics-with-language-informed-vision-models/
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