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Titlebook: Bioinformatics Research and Application; 7th International Sy Jianer Chen,Jianxin Wang,Alexander Zelikovsky Conference proceedings 2011 Spr

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https://doi.org/10.1007/978-3-319-70736-5ase susceptibility. A modern statistical method for approaching this problem is through model selection (or structure estimation) of Structured Input-Output Regression Models (SIORM) fitted on genetic and phenotypic variation data across a large number of individuals.
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https://doi.org/10.1007/978-3-319-70736-5odshave been proposed to identify essential proteins from protein-protein interaction (PPI) network.However most of methods only use the PPI network topology information. HartGT indicated that essentiality is a product of theprotein complex rather than the individual protein. Based on these, we prop
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https://doi.org/10.1007/978-3-319-70736-5vo drug design, structure identification and comparison of functional sites. Structural genomic projects are beginning to produce protein structures with unknown functions. Therefore, efficient methods are required if all these structures are to be properly annotated. When comparing a complete prote
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https://doi.org/10.1007/978-3-319-70736-5d efficient similarity search in RNA structural databases. Here, we propose a method called . (RNA .condary s.ructure-based .tiary structure similarity) based on partitioning of RNA structures into so-called generalized secondary structure units (GSSU). We introduce a fast similarity method exploiti
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Some Problems of the Theory of Normsbiological specimens. Blob-based iterative methods are advantageous reconstruction methods for 3D reconstruction of ET, but demand huge computational costs. Multiple Graphic processing units (multi-GPUs) offer an affordable platform to meet these demands, nevertheless, are not efficiently used owing
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https://doi.org/10.1007/978-3-662-08054-2(.) events, and two others (poplar and cucumber) that descend from independent .s, we infer the ancestral gene order of the rosid clade and those of its main subgroups, the fabids and malvids. We use the gene order evidence to evaluate the hypothesis that the order Malpighiales belongs to the malvid
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