Development Of Statistical Methodologies And Application To Clinical Cancer Studies
Funder
National Health and Medical Research Council
Funding Amount
$428,065.00
Summary
Integrating different layers of information coming from the recent ‘-omics’ technologies can help improving the treatment and the prevention of complex diseases. In particular, the identification of molecular markers of different types can be used for better diagnostics and prognosis in cancer and immune diseases. This project will develop innovative statistical solutions to handle and make sense of the vast amount of biological data that are routinely generated in the laboratories.
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.
I am a bioinformatician conducting methodological research in statistical functional genomics. I use designed experiments involving highthroughput gene expression technologies to make inferences about gene function and to make discoveries of medical signi
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.
Prof Speed is a statistician specializing in bioinformatics and computational biology, applying my skills in support of basic research in molecular and cell biology and genetics.
I am a statistician specializing in bioinformatics and computational biology, applying my skills in support of basic research in molecular and cell biology and genetics.
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
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