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.
Bayesian methodology for analysis of genome data with focus on the livestock industry. The aim is to develop statistical methods for the design and analysis of genome data with focus on the special needs of the livestock industry. This will significantly contribute to profitability, quality, genetic improvement and genetic knowledge in a key national industry, improve Australia's international and national profile in the key research area of bioinformatics, and encourage optimisation of current ....Bayesian methodology for analysis of genome data with focus on the livestock industry. The aim is to develop statistical methods for the design and analysis of genome data with focus on the special needs of the livestock industry. This will significantly contribute to profitability, quality, genetic improvement and genetic knowledge in a key national industry, improve Australia's international and national profile in the key research area of bioinformatics, and encourage optimisation of current information. Outcomes include a toolkit of applicable statistical methods, statistically valid algorithms, marketable methods for gene discovery, technology transfer, training and publications.Read moreRead less
Classification of Microarray Gene-Expression Data. The broad aim is to provide statistical methodology for the classification of microarray gene-expression data. Microarrays are part of a new biotechnology that allows the monitoring of expression levels for thousands of genes simultaneously. The explosion in microarrays has produced massive quantities of data that require new statistical techniques for analysis in order to exploit their enormous scientific potential. One of the main uses of ....Classification of Microarray Gene-Expression Data. The broad aim is to provide statistical methodology for the classification of microarray gene-expression data. Microarrays are part of a new biotechnology that allows the monitoring of expression levels for thousands of genes simultaneously. The explosion in microarrays has produced massive quantities of data that require new statistical techniques for analysis in order to exploit their enormous scientific potential. One of the main uses of the methodology to be developed is to expedite the discovery of new subclasses of diseases. Another is to provide prediction rules for the diagnosis and treatment of diseases.Read moreRead less
New Directions in Bayesian Statistics: formulation, computation and application to exemplar challenges. Bayesian statistics is a fundamental statistical and machine learning approach for density estimation, data analysis and inference. However, there remain open questions regarding the formulation of the model, the likelihood and priors, and efficient computation. This project proposes new approaches that address these issues, and applies them to two exemplar challenges: the impact of climate ch ....New Directions in Bayesian Statistics: formulation, computation and application to exemplar challenges. Bayesian statistics is a fundamental statistical and machine learning approach for density estimation, data analysis and inference. However, there remain open questions regarding the formulation of the model, the likelihood and priors, and efficient computation. This project proposes new approaches that address these issues, and applies them to two exemplar challenges: the impact of climate change on the Great Barrier Reef and better understanding neurological diseases related aging, in particular Parkinson's Disease. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130100819
Funder
Australian Research Council
Funding Amount
$281,600.00
Summary
Measuring the improbable: optimal Monte Carlo methods for rare event simulation of maxima of dependent random variables. Some events occurring with low frequency can have dramatic consequences: natural catastrophes, economic crises, system malfunctions. Estimating their probabilities is a very difficult problem. This project will develop new simulation methods capable of delivering the most precise and efficient estimators for the probabilities of such events.
Random network models with applications in biology. Complex biological systems consist of a large number of interacting agents or components, and so can be studied using mathematical random network models. We aim to gain deeper insights into the laws emerging as the random networks evolve in time. This can help us to deal with dangerous disease epidemics and better understand the human brain.
Robust inferences for analysis of longitudinal data. This project will develop novel statistical tools. Outcomes of this project will enable more reliable data analysis and more cost effective designs in environmental and biological studies.
Discovery Early Career Researcher Award - Grant ID: DE160100741
Funder
Australian Research Council
Funding Amount
$382,274.00
Summary
Tractable Bayesian algorithms for intractable Bayesian problems. This project seeks to develop computationally efficient and scalable Bayesian algorithms to estimate the parameters of complex models and ensure inferences drawn from the models can be trusted. Bayesian parameter estimation and model validation procedures are currently computationally intractable for many complex models of interest in science and technology. These include biological processes such as the efficacy of heart disease, ....Tractable Bayesian algorithms for intractable Bayesian problems. This project seeks to develop computationally efficient and scalable Bayesian algorithms to estimate the parameters of complex models and ensure inferences drawn from the models can be trusted. Bayesian parameter estimation and model validation procedures are currently computationally intractable for many complex models of interest in science and technology. These include biological processes such as the efficacy of heart disease, wound healing and skin cancer treatments. Potential outcomes of the project include new algorithms to significantly economise computations and improved understanding of the mechanisms of experimental data generation. Improved models of wound healing, skin cancer growth and heart physiology supported by these algorithms could improve population health.Read moreRead less
Random Discrete Structures: Approximations and Applications. The behaviour of many real world systems can be modelled by random discrete structures evolving over time. For example, the sizes of populations of frogs in some close patches of forests can be modelled as interacting random processes. The aim of the project is to investigate large discrete random structures that arise from real world application in areas such as biology, complex networks and insurance. The proposed project is at the i ....Random Discrete Structures: Approximations and Applications. The behaviour of many real world systems can be modelled by random discrete structures evolving over time. For example, the sizes of populations of frogs in some close patches of forests can be modelled as interacting random processes. The aim of the project is to investigate large discrete random structures that arise from real world application in areas such as biology, complex networks and insurance. The proposed project is at the interface of mathematics and 'big data' applications and so the work of the project aims to provide theoretical and heuristic underpinnings useful in the algorithms and techniques of practitioners. Understanding the applications in the project requires new, broadly applicable methods and developing such is a complementary aim.Read moreRead less
Understanding the effects of individual variation on population dynamics. Recent empirical studies have shown that trait variation among individuals in a population can have a significant impact on population dynamics. Given the considerable resources devoted to managing populations in Australia, it is vital individual variation be understood. This project will use the tools of modern probability theory to investigate the effect of trait variation on population-level quantities, such as the prob ....Understanding the effects of individual variation on population dynamics. Recent empirical studies have shown that trait variation among individuals in a population can have a significant impact on population dynamics. Given the considerable resources devoted to managing populations in Australia, it is vital individual variation be understood. This project will use the tools of modern probability theory to investigate the effect of trait variation on population-level quantities, such as the probability of extinction and the long term equilibrium level. This work will lead to better strategies for managing invasive diseases and pests, thus helping to protect Australia's biodiversity. The methods developed will be applicable to areas beyond population dynamics.Read moreRead less