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Full-Text Articles in Computational Biology
Cross-Ontology Multi-Level Association Rule Mining In The Gene Ontology., Prashanti Manda, Seval Ozkan, Hui Wang, Fiona M. Mccarthy, Susan M. Bridges
Cross-Ontology Multi-Level Association Rule Mining In The Gene Ontology., Prashanti Manda, Seval Ozkan, Hui Wang, Fiona M. Mccarthy, Susan M. Bridges
Bagley College of Engineering Publications and Scholarship
The Gene Ontology (GO) has become the internationally accepted standard for representing function, process, and location aspects of gene products. The wealth of GO annotation data provides a valuable source of implicit knowledge of relationships among these aspects. We describe a new method for association rule mining to discover implicit co-occurrence relationships across the GO sub-ontologies at multiple levels of abstraction. Prior work on association rule mining in the GO has concentrated on mining knowledge at a single level of abstraction and/or between terms from the same sub-ontology. We have developed a bottom-up generalization procedure called Cross-Ontology Data Mining-Level by …
Testing Phylogenetic Hypotheses Of The Subgenera Of The Freshwater Crayfish Genus Cambarus (Decapoda: Cambaridae)., Jesse W Breinholt, Megan L Porter, Keith A Crandall
Testing Phylogenetic Hypotheses Of The Subgenera Of The Freshwater Crayfish Genus Cambarus (Decapoda: Cambaridae)., Jesse W Breinholt, Megan L Porter, Keith A Crandall
Computational Biology Institute
BACKGROUND: The genus Cambarus is one of three most species rich crayfish genera in the Northern Hemisphere. The genus has its center of diversity in the Southern Appalachians of the United States and has been divided into 12 subgenera. Using Cambarus we test the correspondence of subgeneric designations based on morphology used in traditional crayfish taxonomy to the underlying evolutionary history for these crayfish. We further test for significant correlation and explanatory power of geographic distance, taxonomic model, and a habitat model to estimated phylogenetic distance with multiple variable regression.
METHODOLOGY/PRINCIPAL FINDINGS: We use three mitochondrial and one nuclear gene …