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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.
Developing bioinformatics methods for single cell transcriptomics. This project aims to develop novel bioinformatics methods for single cell transcriptomic data that seek to model variability in cell populations. The project expects to generate new approaches using Bayesian statistics that will act as high-end enablers of discovery in transcriptional regulatory processes. Through an interdisciplinary combination of experimental and computational research, insights into fundamental biological pro ....Developing bioinformatics methods for single cell transcriptomics. This project aims to develop novel bioinformatics methods for single cell transcriptomic data that seek to model variability in cell populations. The project expects to generate new approaches using Bayesian statistics that will act as high-end enablers of discovery in transcriptional regulatory processes. Through an interdisciplinary combination of experimental and computational research, insights into fundamental biological processes will be elucidated, specifically the robustness of cellular systems. Expected outcomes include a suite of novel tools that will push the boundaries of current bioinformatics solutions with potential to deliver significant benefits to every domain of biological science, particularly tissue engineering and synthetic biology.Read moreRead less
Stochastic modelling of genetic regulatory networks with burst process. This project will develop the next generation of stochastic modelling to study the fundamental principles of genetic regulation. Simulations will yield deeper insight into the origin of bistability and oscillation in gene networks.