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X-WR-CALNAME:Sydney Precision Data Science Centre
X-ORIGINAL-URL:https://spds.sydney.edu.au
X-WR-CALDESC:Events for Sydney Precision Data Science Centre
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TZID:Australia/Sydney
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20241104T080000
DTEND;TZID=Australia/Sydney:20241104T170000
DTSTAMP:20260413T030045
CREATED:20241028T004554Z
LAST-MODIFIED:20241028T004555Z
UID:603-1730707200-1730739600@spds.sydney.edu.au
SUMMARY:COMBINE Student Symposium
DESCRIPTION:The COMBINE Student Symposium is back in 2024! Visit the symposium website for all the details https://www.combine.org.au/symp/symposium-2024/
URL:https://spds.sydney.edu.au/event/combine-student-symposium/
ATTACH;FMTTYPE=image/png:https://spds.sydney.edu.au/wp-content/uploads/2024/10/COMBINE-logo.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20241018T080000
DTEND;TZID=Australia/Sydney:20241018T170000
DTSTAMP:20260413T030045
CREATED:20251030T234700Z
LAST-MODIFIED:20241025T031617Z
UID:288-1729238400-1729270800@spds.sydney.edu.au
SUMMARY:Weekly Seminar - Week 1
DESCRIPTION:Statistical Bioinformatics Seminar\n\n\n\n\n\n\n\nCaption here\n\n\n\n\n\nLorem ipsum dolor sit amet\, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam\, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident\, sunt in culpa qui officia deserunt mollit anim id est laborum. \n\n\n\nCurabitur pretium tincidunt lacus. Nulla gravida orci a odio. Nullam varius\, turpis et commodo pharetra\, est eros bibendum elit\, nec luctus magna felis sollicitudin mauris. Integer in mauris eu nibh euismod gravi…
URL:https://spds.sydney.edu.au/event/weekly-seminar-week-1/
LOCATION:Eastern Avenue Lecture Theatre\, Eastern Avenue - The University of Sydney\, Camperdown\, NSW\, 2006
CATEGORIES:Weekly Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20241014T130000
DTEND;TZID=Australia/Sydney:20241014T140000
DTSTAMP:20260413T030045
CREATED:20241025T031405Z
LAST-MODIFIED:20241025T031405Z
UID:351-1728910800-1728914400@spds.sydney.edu.au
SUMMARY:Harnessing the power of AI in biotechnology\, personalised medicine\, drug development and beyond
DESCRIPTION:Statistical Bioinformatics Seminar: Judith and David Coffey Speaker\n\n\n\nSpeaker: Prof David Ascher (University of Queensland) \n\n\n\nAbstract: We have developed a comprehensive computational platform that uses graph-based signatures to represent the wild-type environment of a residue in order to predict the structural and functional effects of mutations. This platform has been used to explore the effects of genetic disease and drug resistance mutations on protein folding\, stability\, dynamics and interactions\, and their links to mutational tolerance and phenotypes. Mutations leading to larger molecular consequences\, tended to be rarer\, and needed the presence of compensatory mutations balancing these fitness costs to become fixed in a population. \n\n\n\nWe have now successfully clinically translated methods that use insights on the 3D effects of mutations to guide patient risk management in genetic diseases\, and in the pre-emptive detection of drug resistance mutations in tuberculosis (rifampicin and pyrazinamide resistance). It has also been applied as part of drug development pipelines to guide design of drugs less prone to resistance. \n\n\n\nThis work has highlighted that structural bioinformatics tools\, when applied in a systematic\, integrated way\, can provide a powerful and scalable approach for predicting structural and functional consequences of mutations in order to reveal molecular mechanisms leading to clinical and experimental phenotypes. These computational tools are freely available (http://biosig.unimelb.edu.au/biosig/tools). \n\n\n\nAbout the speaker: David Ascher is Deputy Associate Dean of Research and Director of Biotechnology Program at The University of Queensland\, Deputy Director of the Australian Centre for Ecogenomics and Head of the Computational Biology and Clinical Informatics laboratory at the Baker Institute. David’s research focus is in modelling biological data to gain insight into fundamental biological processes. One of his primary research interests has been developing tools to unravel the link between genotype and phenotype\, using computational and experimental approaches to understand the effects of mutations on protein structure and function. His group has developed a platform of 65 widely used programs for assessing the molecular consequences of coding variants. This platform has now been successfully translated into clinical use to guide the diagnosis\, management and treatment of a number of hereditary diseases\, rare cancers and drug resistant infections\, and into industry as part of drug development pipelines to guide design of safer and more effective therapeutics.
URL:https://spds.sydney.edu.au/event/harnessing-the-power-of-ai-in-biotechnology-personalised-medicine-drug-development-and-beyond/
LOCATION:Charles Perkins Centre\, University of Sydney\, Johns Hopkins Dr\, Sydney\, NSW\, 2050\, Australia
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20240930T130000
DTEND;TZID=Australia/Sydney:20240930T140000
DTSTAMP:20260413T030045
CREATED:20241025T031405Z
LAST-MODIFIED:20241101T052902Z
UID:354-1727701200-1727704800@spds.sydney.edu.au
SUMMARY:Chatting About Pathology
DESCRIPTION:Statistical Bioinformatics Seminar\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSpeaker: Dr Drew Williamson (Emory University) \n\n\n\nAbstract: Generative models such as ChatGPT have catapulted AI further into the mainstream than ever before\, while interested parties in healthcare have begun to test these models in medical settings. However\, these models are rarely purpose-built for medical applications and the majority of those that do exist are targeted at a broader segment of healthcare than any individual provider practices. I’ll discuss the design\, training\, and evaluation of PathChat\, our multimodal generative model created specifically for pathology. I’ll also discuss possible future directions in this line of inquiry. \n\n\n\nAbout the speaker: Drew Williamson completed a BA in Mathematics at Oberlin College\, an MD at Case Western Reserve University\, residency in Anatomic Pathology at Brigham & Women’s Hospital\, and fellowships in Molecular Genetic Pathology and Clinical Informatics at Mass General Brigham. This training was followed by postdoctoral research in the lab of Faisal Mahmood\, PhD at Brigham & Women’s Hospital and Harvard Medical School. A board certified pathologist\, Drew’s research focuses on the applications of deep learning-based methods to pathology data\, from histology images to genomics to\, most recently\, natural language. He is now an Assistant Professor in the department of Pathology & Laboratory Medicine at Emory University.
URL:https://spds.sydney.edu.au/event/chatting-about-pathology/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20240422T130000
DTEND;TZID=Australia/Sydney:20240422T140000
DTSTAMP:20260413T030045
CREATED:20241025T031405Z
LAST-MODIFIED:20241108T045121Z
UID:344-1713790800-1713794400@spds.sydney.edu.au
SUMMARY:Systematic comparison of sequencing-based spatial transcriptomic methods with cadasSTre and SpatialBen
DESCRIPTION:Statistical Bioinformatics Seminar: Judith and David Coffey Speaker\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSpeaker: Prof Matthew Ritchie (WEHI) \n\n\n\nAbstract: Sequencing-based Spatial Transcriptomics (sST) allows gene expression to be measured within complex tissue contexts. Although a wide array of sST technologies are currently available to researchers\, efforts to comprehensively benchmark different platforms are currently lacking. The inherent variability across technologies and datasets poses challenges in formulating standardized evaluation metrics. To address this\, we established a collection of reference tissues and regions characterized by well-defined histological architecture and other biological ground truth and used them to generate the cadasSTre and SpatialBench datasets that compare 11 sST methods. We highlight molecular diffusion as a variable parameter across different methods and tissues\, significantly impacting the effective resolution. Furthermore\, we observed that spatial transcriptomic data demonstrate unique attributes beyond merely adding a spatial axis to single-cell data\, including an enhanced ability to capture patterned rare cell states along with specific markers\, albeit being influenced by multiple factors including sequencing depth and resolution. For the 10X Visium platform\, we benchmarked the performance of different sample handling approaches after preprocessing\, explored spatially variable gene detection and the ability of clustering and cell deconvolution to identify expected cell types and tissue regions. Multi-sample differential expression analysis was able to recover known gene signatures related to biological sex or gene knockout. Our datasets and analyses serve as a practical guide for sST users and will be useful in future benchmarking studies. \n\n\n\nAbout the speaker: Professor Matt Ritchie has been at lab head at the WEHI for the past 11 years. His team develops analysis methods and open-source software tailored to new applications of genomic technology in biomedical research. In the single-cell and spatial biology field\, this work includes tools for data preprocessing (scPipe)\, benchmarking at scale (CellBench) and new protocols and analysis methods (FLAMES) for applying long-read sequencing to single-cell research. His most recent research is on developing benchmarking resources for sequencing-based spatial transcriptomics technologies (cadasSTre and SpatialBench). Matt completed his PhD on microarray data analysis at WEHI in 2005 under the supervision of Professor Gordon Smyth\, which was followed by a period of post-doctoral research at the EBI (Hinxton\, UK) and University of Cambridge before returning to WEHI as a Senior Research Officer in 2008. He is a keen advocate of open-source software\, having served on both the Technical Advisory Board and Community Advisory Board of the Bioconductor project.
URL:https://spds.sydney.edu.au/event/systematic-comparison-of-sequencing-based-spatial-transcriptomic-methods-with-cadasstre-and-spatialben/
LOCATION:Charles Perkins Centre\, University of Sydney\, Johns Hopkins Dr\, Sydney\, NSW\, 2050\, Australia
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2024/10/nutriomics-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20240415T130000
DTEND;TZID=Australia/Sydney:20240415T140000
DTSTAMP:20260413T030045
CREATED:20241025T031405Z
LAST-MODIFIED:20241025T031405Z
UID:347-1713186000-1713189600@spds.sydney.edu.au
SUMMARY:High dimensional tensor methods for multi-modal single cell genomics data
DESCRIPTION:Statistical Bioinformatics Seminar\n\n\n\nSpeaker: Kwangmoon Park (University of Wisconsin-Madison) \n\n\n\nAbstract: Emerging single cell technologies that simultaneously capture long-range interactions of genomic loci together with their DNA methylation levels are advancing our understanding of 3D genome structure and its interplay with the epigenome at the single cell level. While methods to analyze data from single cell high throughput chromatin conformation capture (scHi-C) experiments are maturing\, methods that can jointly analyze multiple modalities with scHi-C data are lacking. In this talk\, I present two tensor modeling frameworks: Muscle and SHOPS\, to jointly analyze 3D conformation and DNA methylation data measured at the single cell level. First\, I present Muscle\, a joint decomposition of Multiple single cell tensors. Muscle is a novel tensor decomposition method that can integrate the scHi-C and DNA methylation modalities with a direct interpretability. Next\, I introduce SHOPS\, Sparse Higher Order Partial Least Squares\, which provides an inference on the direct association between Hi-C and DNA methylation. SHOPS is a new tensor response regression method to simultaneously achieve denoising of the scHi-C tensor and selecting the most relevant methylation sites with dimension reduction. \n\n\n\nAbout the speaker: Kwangmoon Park is a Statistics Ph.D. Candidate at the University of Wisconsin-Madison. He is currently working on statistical genomics and high dimensional statistics with Professor Sündüz Keleş. Before joining UW-Madison\, he earned a master’s degree in Statistics at the Yonsei University in 2020. He earned a B.A. in Economics and Statistics at Yonsei University in Korea and studied Economics as an exchange student at Erasmus Universiteit Rotterdam in the Netherlands. Kwangmoon Park is mainly interested in questions related to understanding how genes are regulated by distal regions in the genome\, particularly by functional non-coding regions. For that purpose\, he develops statistical tools for analyzing High-dimensional genomic data\, including Hi-C and HiChIP\, and for linking diverse types of genomic or epigenomic data with better statistical interpretation. The statistical methodologies he works on are related to tensor factorization/regression and dimension reduction techniques\, including Partial Least Squares. \n\n\n\nJoin on Zoom: https://uni-sydney.zoom.us/j/84087321707
URL:https://spds.sydney.edu.au/event/high-dimensional-tensor-methods-for-multi-modal-single-cell-genomics-data/
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