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dc.contributorPeter H. McCartney Program Manageren_US
dc.contributorMurali Ramanathan |Bianca Weinstock-Guttman |Maurizio Trevisan |Robert Zivadinov |en_US
dc.contributor.authorZhang, Aidong Principal Investigatoren_US
dc.contributor.otherazhang@buffalo.eduen_US
dc.dateApril 30, 2012en_US
dc.date.accessioned2011-04-08T19:25:09Zen_US
dc.date.accessioned2011-04-19T18:33:56Z
dc.date.availableMay 1, 2003en_US
dc.date.available2011-04-08T19:25:09Zen_US
dc.date.available2011-04-19T18:33:56Z
dc.date.issued2011-04-08T19:25:09Zen_US
dc.identifier0234895en_US
dc.identifier0234895en_US
dc.identifier.urihttp://hdl.handle.net/10477/1265
dc.descriptionGrant Amount: $ 1628007en_US
dc.description.abstractThe over-availability of data and the under-availability of knowledge present a critical challenge for biological informatics in the years to come. Clearly, effective techniques are need not only for storage and retrieval purposes, but also for mining genomic data to increase our knowledge. However, the high dimensionality and enormous size of genomic data pose very challenging problems in analysis and visualization of the data sets. This project investigates novel approaches to analyzing gene expression data and integrating them into biological research. New algorithms and tools that can be used iteratively and interactively to mine the data will be developed. The strategies include a meta data hierarchy for integration of heterogeneous data, cluster-based indexing for high-dimensional data, inter-dimensional analysis for classification, and dynamic interactive visualization for pattern analysis. The approaches will be field-tested by biologists investigating an organism's phenotype and genotype iterations. The project will deliver a flexible, scalable workbench environment ready to be used for general genomic data analysis. In addition to education development activities, the project's impact will be enhanced by broad applications in other fields that handle large-scale multi-dimensional data sets.en_US
dc.titleAdvanced Approaches for Integration and Analysis of Genomic Dataen_US
dc.typeNSF Granten_US


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