Computing and Information Systems - Research Publications
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ItemFast and accurate protein substructure searching with simulated annealing and GPUs(BMC, 2010-09-03)BACKGROUND: Searching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif) searching. RESULTS: We developed an improved heuristic for tableau-based protein structure and substructure searching using simulated annealing, that is as fast or faster and comparable in accuracy, with some widely used existing methods. Furthermore, we created a parallel implementation on a modern graphics processing unit (GPU). CONCLUSIONS: The GPU implementation achieves up to 34 times speedup over the CPU implementation of tableau-based structure search with simulated annealing, making it one of the fastest available methods. To the best of our knowledge, this is the first application of a GPU to the protein structural search problem.
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ItemStructuring Documents Efficiently(University of Sydney, 2005)
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ItemPrincipal type inference for GHC-style multi-parameter type classes(SPRINGER-VERLAG BERLIN, 2006)
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ItemLazy Clause Generation: Combining the Power of SAT and CP (and MIP?) Solving(SPRINGER-VERLAG BERLIN, 2010)
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ItemOptimizing compilation of CHR with rule priorities(Springer, 2008-05-14)
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ItemObservable confluence for constraint handling rules(SPRINGER-VERLAG BERLIN, 2007)
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ItemTelecommunications feature subscription as a partial order constraint problem(Springer Verlag, 2008)
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ItemDiscovery of minimal unsatisfiable subsets of constraints using hitting set dualization(SPRINGER-VERLAG BERLIN, 2005)
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ItemPropagating Dense Systems of Integer Linear Equations(ASSOC COMPUTING MACHINERY, 2007)
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ItemMIRAGAA-a methodology for finding coordinated effects of microRNA expression changes and genome aberrations in cancer(OXFORD UNIV PRESS, 2010-01-15)MOTIVATION: Cancer evolves through microevolution where random lesions that provide the biggest advantage to cancer stand out in their frequent occurrence in multiple samples. At the same time, a gene function can be changed by aberration of the corresponding gene or modification of microRNA (miRNA) expression, which attenuates the gene. In a large number of cancer samples, these two mechanisms might be distributed in a coordinated and almost mutually exclusive manner. Understanding this coordination may assist in identifying changes which significantly produce the same functional impact on cancer phenotype, and further identify genes that are universally required for cancer. Present methodologies for finding aberrations usually analyze single datasets, which cannot identify such pairs of coordinating genes and miRNAs. RESULTS: We have developed MIRAGAA, a statistical approach, to assess the coordinated changes of genome copy numbers and miRNA expression. We have evaluated MIRAGAA on The Cancer Genome Atlas (TCGA) Glioblastoma Multiforme datasets. In these datasets, a number of genome regions coordinating with different miRNAs are identified. Although well known for their biological significance, these genes and miRNAs would be left undetected for being less significant if the two datasets were analyzed individually. AVAILABILITY AND IMPLEMENTATION: The source code, implemented in R and java, is available from our project web site at http://www.csse.unimelb.edu.au/~rgaire/MIRAGAA/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.