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.
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
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