Prognosis based network-type feature extraction for complex biological data. This project aims to develop statistical tools that integrate high-throughput molecular data with biological knowledge to make discoveries in complex diseases. This project uses machine learning methods, statistical models and proteomic platforms to identify relationships among clinico-pathologic and molecular measurements. It will produce tools and insights that are intended to accelerate the process of biologically an ....Prognosis based network-type feature extraction for complex biological data. This project aims to develop statistical tools that integrate high-throughput molecular data with biological knowledge to make discoveries in complex diseases. This project uses machine learning methods, statistical models and proteomic platforms to identify relationships among clinico-pathologic and molecular measurements. It will produce tools and insights that are intended to accelerate the process of biologically and clinically significant discoveries in biomedical research. This project will help Australian researchers in statistics and users of statistics (from fields as diverse as biology, ecology, medicine, finance, agriculture and the social sciences) to make better predictions that are easier to understand.Read moreRead less
Empirical and computational solutions for multi-omics single-cell assays. Emerging single-cell sequencing technologies are transforming molecular cell biology, but identifying novel cell types and their functions requires the integration of highly heterogeneous data. The development of computational methods able to extract biologically relevant results is hindered by the lack of high-quality datasets. This project aims to develop novel sequencing methodologies and generate data to drive our dime ....Empirical and computational solutions for multi-omics single-cell assays. Emerging single-cell sequencing technologies are transforming molecular cell biology, but identifying novel cell types and their functions requires the integration of highly heterogeneous data. The development of computational methods able to extract biologically relevant results is hindered by the lack of high-quality datasets. This project aims to develop novel sequencing methodologies and generate data to drive our dimension reduction multivariate method developments for data integration. By combining in silico and in vivo approaches, the project is anticipated to benefit scientists willing to work in cutting-edge single-cell research by providing useful protocols and tools to generate novel insights in cell biology. Read moreRead less
Vertically integrated statistical modelling in multi-layered omics studies. This project will develop an adaptive statistical modelling framework that uses information from many omics data to discover a collection of stable and clinically significant biomarkers. Results will enable researchers to better understand the underlying biological system of complex diseases such as cancer, Alzheimer and diabetes.
Discovery Early Career Researcher Award - Grant ID: DE180101252
Funder
Australian Research Council
Funding Amount
$343,450.00
Summary
Statistical theory and algorithms for joint inference of complex networks. This project aims to address the challenges in jointly modelling complex networks by applying an integrated approach encompassing statistical theory, computation, and applications. The project expects to contribute to core statistical methodology development for complex inference and generate new knowledge in the fields of genomics, neuroscience, and social science through in-depth analyses of large-scale multilayered net ....Statistical theory and algorithms for joint inference of complex networks. This project aims to address the challenges in jointly modelling complex networks by applying an integrated approach encompassing statistical theory, computation, and applications. The project expects to contribute to core statistical methodology development for complex inference and generate new knowledge in the fields of genomics, neuroscience, and social science through in-depth analyses of large-scale multilayered network data. Expected outcomes include enhanced theoretical and computational frameworks for probabilistic network models to better utilise the power of multiple observations. This should foster international and interdisciplinary collaborations and add significant value to the rapidly progressing field of networks research.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100944
Funder
Australian Research Council
Funding Amount
$427,068.00
Summary
Statistical frameworks for high-parameter imaging cytometry data. The project aims to develop statistical and bioinformatics methodology for characterising the complex interactions between cells in their native environment. Recent advances in imaging cytometry technologies have made it possible to observe the behaviour of multiple cell-types in tissue concurrently. The intended outcome is a suite of statistical methodologies that are crucial for addressing a variety of biological problems with t ....Statistical frameworks for high-parameter imaging cytometry data. The project aims to develop statistical and bioinformatics methodology for characterising the complex interactions between cells in their native environment. Recent advances in imaging cytometry technologies have made it possible to observe the behaviour of multiple cell-types in tissue concurrently. The intended outcome is a suite of statistical methodologies that are crucial for addressing a variety of biological problems with these state-of-the-art technologies. This work will advance knowledge in bioinformatics, statistics and image analysis, providing benefits to scientists studying the fundamental behaviour of cells and underlying disease mechanisms.Read moreRead less
Evolution and functional impact of gene silencing by hairpin derived RNAs. This project aims to study RNA-mediated gene silencing in genome evolution. RNA interference (RNAi) has been widely used as an experimental tool since its Nobel Prize-winning discovery in 1998, but little is known about endogenous RNAi or its evolution. This project uses bioinformatics, high-throughput sequencing and molecular approaches to study hpRNAs, a class of small interfering RNAs, their adaptive evolution across f ....Evolution and functional impact of gene silencing by hairpin derived RNAs. This project aims to study RNA-mediated gene silencing in genome evolution. RNA interference (RNAi) has been widely used as an experimental tool since its Nobel Prize-winning discovery in 1998, but little is known about endogenous RNAi or its evolution. This project uses bioinformatics, high-throughput sequencing and molecular approaches to study hpRNAs, a class of small interfering RNAs, their adaptive evolution across fly species and vertebrates, and their functional effect on testis morphogenesis and distortion of female/male sex-ratio. The project also studies splicing-dependent small RNAs and miRNA-target interaction. This research could have applications from animal development to human pathology.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100964
Funder
Australian Research Council
Funding Amount
$443,869.00
Summary
Statistical approaches for spatial genomics at single cell resolution. Cells cooperate to form complex, dynamic and varied tissue structures. This project aims to develop statistical and computational approaches to analyse spatial genomics data, a novel technology that retains vital spatial information at single cell resolution while detecting RNA molecules for hundreds of genes. Observing the molecular activity of cells in their spatial context is critical for tackling key biological questions, ....Statistical approaches for spatial genomics at single cell resolution. Cells cooperate to form complex, dynamic and varied tissue structures. This project aims to develop statistical and computational approaches to analyse spatial genomics data, a novel technology that retains vital spatial information at single cell resolution while detecting RNA molecules for hundreds of genes. Observing the molecular activity of cells in their spatial context is critical for tackling key biological questions, such as how tumour cells behave during malignancy or how stem cells determine their fate. Expected outcomes also include techniques to fully harmonise spatial and non-spatial genomics datasets, and methods toward understanding the complex relationships among cells in their environment, revealing novel cell biology.Read moreRead less
The Stemformatics gene expression compendium: development of multivariate statistical approaches for cross platform analyses. Scientific data is gathered in many different forms, but there are significant gaps in our ability to analyse multiple datasets when generated on different pieces of equipment. This project will study three typical research questions in stem cell biology to develop new analytical approaches to help solve this major data gap.