Reconstructing proteins to explain and engineer biological diversity. The aim of this project is to develop computational methods to construct entirely new proteins. Computational reconstruction of enzymes that have been extinct for over 400 million years has revealed remarkable opportunities for biotechnological innovation. The intended outcomes are to develop bioinformatics methods to broaden the scope of ancestral protein reconstruction to include protein super-families, to establish what spe ....Reconstructing proteins to explain and engineer biological diversity. The aim of this project is to develop computational methods to construct entirely new proteins. Computational reconstruction of enzymes that have been extinct for over 400 million years has revealed remarkable opportunities for biotechnological innovation. The intended outcomes are to develop bioinformatics methods to broaden the scope of ancestral protein reconstruction to include protein super-families, to establish what specific changes led to the evolutionary success of a protein, and to re-run evolution to generate proteins that perform in conditions suitable for industrial and agricultural applications, in particular the production of hydroxylated fatty acids for bioplastics. By examining proteins from many life forms, the project plans to develop a novel bioinformatics strategy to understand their evolution and engineer new proteins for use in production of chemical commodities.Read moreRead less
Mining large negative correlations for high-dimensional contrasting analysis. Negative correlations are widely embedded in real life applications, but in-depth research has rarely been conducted due to its high level of complexity. This project aims at efficient algorithms and frontier theory for finding large negative correlations, to enable smart information use in bioinformatics to promote Australia's leading role in data mining research.
Deep correction of DNA sequencing errors by data mining algorithms. This project aims to investigate the many layers of error correction problems in the terabytes of genomic sequence data, and aims to solve these problems by novel data mining algorithms. High-throughput sequencing platforms have generated massive amounts of useful raw data, but also made widespread errors. The new algorithms are capable of correcting errors at deeper layers to further enhance data quality. Expected outcome inclu ....Deep correction of DNA sequencing errors by data mining algorithms. This project aims to investigate the many layers of error correction problems in the terabytes of genomic sequence data, and aims to solve these problems by novel data mining algorithms. High-throughput sequencing platforms have generated massive amounts of useful raw data, but also made widespread errors. The new algorithms are capable of correcting errors at deeper layers to further enhance data quality. Expected outcome includes the knowledge advancement of genomic data industry and interdisciplinary collaboration between biotechnology and data mining. This also provides significant benefit for genomic decisions in forensics and personalised medicine which demand accurate genomic information.Read moreRead less
Efficient data mining methods for evidence-based decision making. This project aims to develop efficient data mining methods for causal predictions. Evidence-based decision making (EBD), such as evidence-based medicine and policy, is always preferable. To support EBD, causal predictions forecast how outcomes change when conditions are manipulated. Progress has been made in theoretical research on causal inference based on observational data, but few methods can automatically mine causal signals ....Efficient data mining methods for evidence-based decision making. This project aims to develop efficient data mining methods for causal predictions. Evidence-based decision making (EBD), such as evidence-based medicine and policy, is always preferable. To support EBD, causal predictions forecast how outcomes change when conditions are manipulated. Progress has been made in theoretical research on causal inference based on observational data, but few methods can automatically mine causal signals from the data and methods for efficient causal predictions based on data are even fewer. This project will apply its methods to biomedical problems. The outcomes could support smart and data-driven evidence based decision making in many areas, such as therapeutics and government policy making.Read moreRead less
Next-generation techniques for analysing massive data sets. To process enormous amounts of data, leading computing companies are turning to modern computing frameworks, for which little theory of efficient computational techniques has been developed. This project will resolve key theoretical questions and provide fast techniques for poorly understood pattern recognition and bioinformatics problems.
Multiscale integration of imaging and omics data. This project aims to integrate multiscale imaging and molecular data to characterise disease in patients. Modern healthcare needs to embrace ‘big (health) data’s potential to address an ageing population’s increasing healthcare demands and the inefficiencies and waste in patient treatment. This project expects to pioneer basic science research in methodologies to integrate, correlate and then derive knowledge from multi-scale data, to characteris ....Multiscale integration of imaging and omics data. This project aims to integrate multiscale imaging and molecular data to characterise disease in patients. Modern healthcare needs to embrace ‘big (health) data’s potential to address an ageing population’s increasing healthcare demands and the inefficiencies and waste in patient treatment. This project expects to pioneer basic science research in methodologies to integrate, correlate and then derive knowledge from multi-scale data, to characterise the mechanisms of disease in individual patients, in space and time. Its integrated model is expected to form the basis of a framework for individualised patient disease analysis.Read moreRead less
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