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dc.contributorVasant G. Honavar Program Manageren_US
dc.contributorMichael Buck |en_US
dc.contributor.authorZhang, Aidong Principal Investigatoren_US
dc.contributor.otherazhang@buffalo.eduen_US
dc.dateJuly 31, 2011en_US
dc.date.accessioned2011-04-08T19:26:51Zen_US
dc.date.accessioned2011-04-19T18:33:39Z
dc.date.availableAugust 15, 2010en_US
dc.date.available2011-04-08T19:26:51Zen_US
dc.date.available2011-04-19T18:33:39Z
dc.date.issued2011-04-08T19:26:51Zen_US
dc.identifier1016929en_US
dc.identifier1016929en_US
dc.identifier.urihttp://hdl.handle.net/10477/1223
dc.descriptionGrant Amount: $ 158312en_US
dc.description.abstractThis project forms an interdisciplinary research team with integrated expertise of computer scientists and biomedical scientists to tackle the challenging issues in analyzing protein interaction data. Specifically, the project develops a novel approach to detecting overlapping clusters on emerging large volume of protein-protein interaction data and validates the computational approaches in yeast. The vast amount of protein-protein interaction data provides us with a good opportunity to systematically analyze the structure of a large living system and also allows us to understand essential principles like essentiality, genetic interactions, functions, functional modules, protein complexes, and cellular pathways. The identification of functional modules in protein interaction networks is of great interest because they often reveal unknown functional ties between proteins and hence predict functions for unknown proteins. A protein may be included in one or more functional groups. Therefore, overlapping clusters need to be identified in protein interaction data. This project develops a unique method to integrate domain knowledge with the protein interaction data so that the data will be more reliable. It also develops a unique method to support overlapping modularity analysis for protein interaction data that intelligently integrates biological information into the modularity analysis process. Another unique aspect of this project is the tight integration of computational methods with biological verification. By associating unknown proteins with the known proteins within each functional module, we can suggest that those proteins positively work for the corresponding functions that are assigned to the modules. This project can also find broad applications in other areas which handle data with the modular network property, such as web network, social networks, and technological networks. For further information see the project web page: http://www.cse.buffalo.edu/DBGROUP/PPI-networks/index.htmlen_US
dc.titleIII:Small: Overlapping Clustering Analysis of Biological Networksen_US
dc.typeNSF Granten_US


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