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The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments.
Feilman Fellow; Head, Precision Health Research and Head, Translational Intelligence
We have started a project utilising whole genome sequencing of undiagnosed children living in WA to provide a definitive diagnosis. A major challenge here is that the role and functions of the inter-genic regions of our genome (the remaining 98%) are relatively poorly understood.
We are made up of hundreds of different cell types carrying out a diverse range of functions essential for organism survival. All the information required to specify the morphology, function and response to stimuli of these cells is encoded in identical copies of the genome. The process of gene regu
Current technologies to understand which genes are turned on or off only work on large amounts of biological samples. As a consequence all measurements we receive represent averages across multiple cell types present in the sample. The situation is comparable to studying the contents of a bowl of fr
The Kids Research Institute Australia congratulates Prof Gareth Baynam and Dr Timo Lassmann on their grant over three years from the McCusker Charitable Foundation.
Timo Lassmann BSc (Hons) MSc PhD Feilman Fellow; Head, Precision Health Research and Head, Translational Intelligence timo.lassmann@thekids.org.au
Childhood dementias are a group of rare and ultra-rare paediatric conditions clinically characterised by enduring global decline in central nervous system function, associated with a progressive loss of developmentally acquired skills, quality of life and shortened life expectancy. Traditional research, service development and advocacy efforts have been fragmented due to a focus on individual disorders, or groups classified by specific mechanisms or molecular pathogenesis.
People living with rare diseases (PLWRD) still face huge unmet needs, in part due to the fact that care systems are not sufficiently aligned with their needs and healthcare workforce (HWF) along their care pathways lacks competencies to efficiently tackle rare disease-specific challenges. Level of rare disease knowledge and awareness among the current and future HWF is insufficient.
Computer vision technology is advancing rare disease diagnosis to address unmet needs of the more than 300 million individuals affected globally; one in three rare diseases have a known facial phenotype. 3D face model reconstruction is a key driver of these advances.