Integrating Database Technologies and Visual Analysis in Bioinformatics for Genome Data. Solving modern biological problems, especially those involving genome data, requires advanced computational and analytical methods. The huge quantities of data and escalating demands of modern biological research increasingly require the sophistication and power of object-relational database tools. Key techniques include relational data management, pattern recognition, data mining and visualization of biolog ....Integrating Database Technologies and Visual Analysis in Bioinformatics for Genome Data. Solving modern biological problems, especially those involving genome data, requires advanced computational and analytical methods. The huge quantities of data and escalating demands of modern biological research increasingly require the sophistication and power of object-relational database tools. Key techniques include relational data management, pattern recognition, data mining and visualization of biological data. In this project we will develop efficient methodologies and data structures for gathering high-quality approximations of full genomic information, and will use these innovations as the foundation to develop novel, practical tools for clustering and visualization in genomic data mining and database management.
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Kernel methods for the analysis of whole bacterial genomes. This project addresses the fundamental scientific problem of the identification of regulatory regions and specific promoters within bacterial genomes, with a focus upon two organisms of great social, economic and bioterrism significance. From the machine learning perspective, the project will be the first to produce a kernel-based approach directly tailored to the problem of the detection of regulatory regions. The methods developed wil ....Kernel methods for the analysis of whole bacterial genomes. This project addresses the fundamental scientific problem of the identification of regulatory regions and specific promoters within bacterial genomes, with a focus upon two organisms of great social, economic and bioterrism significance. From the machine learning perspective, the project will be the first to produce a kernel-based approach directly tailored to the problem of the detection of regulatory regions. The methods developed will be made available through a straightforward web-based interface, allowing biologists throughout the world to utilize the approach as a tool to be applied to a progressively widening class of bacterial genomes, and even to eukaryotes. Read moreRead less