Special Statistical Bioinformatics Seminar Dr Jacqueline Siu, Kennedy Institute of Rheumatology, University of Oxford About the seminar: Coordinated global efforts to create a comprehensive map of the human body using single-cell technologies has opened up avenues in computational biology and AI. However, there remains a pressing need to increase the data diversity from the ancestral […]
Professor Bin Yu, Statistics, EECS, Center for Computational Biology, Simons Institute for the Theory of Computing, UC Berkeley About the seminar Alignment and trust are crucial for the successful integration of AI in healthcare including digital twin projects, a field involving diverse stakeholders such as medical personnel, patients, administrators, public health officials, and taxpayers, all […]
Special Judith and David Coffey Seminar Prof Stephen Leslie, The University of MelbourneAbstract: Without the inclusion of diverse genetic ancestries in reference resources, inequity in research and clinical practice risks being entrenched. A handful ethnicities have been the focus of genomic research to date, and Indigenous Australians are virtually absent from global reference panels and […]
The 2024 Bioconductor Asia conference aims to bring together researchers and scientists to exchange scientific knowledge and foster collaboration within the bioinformatics community both across the Asia-Pacific region and globally. The conference will also offer a series of hands-on workshops on R and Bioconductor, aimed at advancing education and training in computational biomedical sciences. Visit […]
The ninth annual Australian Bioinformatics and Computational Biology Society Conference will be hosted at the University of Sydney 4-6 November 2024. Our centre director, research leaders and ECRs are members of the convening committee. Visit the conference website for full details https://www.abacbs.org/conference2024/home
The COMBINE Student Symposium is back in 2024! Visit the symposium website for all the details https://www.combine.org.au/symp/symposium-2024/
Statistical Bioinformatics Seminar Lorem 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. […]
Statistical Bioinformatics Seminar: Judith and David Coffey Speaker Speaker: Prof David Ascher (University of Queensland) Abstract: 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 […]
Statistical Bioinformatics Seminar Speaker: Dr Drew Williamson (Emory University) Abstract: 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 […]
Statistical Bioinformatics Seminar: Judith and David Coffey Speaker Speaker: Prof Matthew Ritchie (WEHI) Abstract: 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 […]
Statistical Bioinformatics Seminar Speaker: Kwangmoon Park (University of Wisconsin-Madison) Abstract: 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 […]
