35 research outputs found

    Heterogeneity of different genome fractions (·10<sup>−2</sup>).

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    <p>The deviation of the sampled median values is less than 0.0001 in all genomes for each of the three scores.</p><p>*H<sub>c</sub>, H<sub>o</sub> and H<sub>t</sub> – compositional, organizational and total heterogeneity scores. Note that H<sub>c</sub> is calculated as median of differences in GC content while H<sub>t</sub> and H<sub>o</sub> scores are evaluated based on comparison of compositional spectra (see section <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032076#s2" target="_blank">Methods</a>);</p><p>**only large chromosomes 1–8 were taken into account.</p

    Predicted and observed values of human intra- and inter-chromosomal heterogeneity estimates.

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    <p>Axes X: predicted values, axes Y: observed values. (A) intra-chromosomal H<sub>t</sub>; (B) average inter-chromosomal H<sub>t</sub>; (C) intra-chromosomal H<sub>o</sub>; (D) average inter-chromosomal H<sub>o</sub>.</p

    The most significant predictors of intra- and inter-chromosomal heterogeneity scores H<sub>t</sub> and H<sub>o</sub>.

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    <p>The most significant predictors of intra- and inter-chromosomal heterogeneity scores H<sub>t</sub> and H<sub>o</sub>.</p

    Application of music therapy techniques in cognitive rehabilitation

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    Table listing the genotype fixed in both control group and desiccation group. (XLSX 2892 kb

    Fast and Accurate Construction of Ultra-Dense Consensus Genetic Maps Using Evolution Strategy Optimization

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    <div><p>Our aim was to develop a fast and accurate algorithm for constructing consensus genetic maps for chip-based SNP genotyping data with a high proportion of shared markers between mapping populations. Chip-based genotyping of SNP markers allows producing high-density genetic maps with a relatively standardized set of marker loci for different mapping populations. The availability of a standard high-throughput mapping platform simplifies consensus analysis by ignoring unique markers at the stage of consensus mapping thereby reducing mathematical complicity of the problem and in turn analyzing bigger size mapping data using global optimization criteria instead of local ones. Our three-phase analytical scheme includes automatic selection of ~100-300 of the most informative (resolvable by recombination) markers per linkage group, building a stable skeletal marker order for each data set and its verification using jackknife re-sampling, and consensus mapping analysis based on global optimization criterion. A novel Evolution Strategy optimization algorithm with a global optimization criterion presented in this paper is able to generate high quality, ultra-dense consensus maps, with many thousands of markers per genome. This algorithm utilizes "potentially good orders" in the initial solution and in the new mutation procedures that generate trial solutions, enabling to obtain a consensus order in reasonable time. The developed algorithm, tested on a wide range of simulated data and real world data (<i>Arabidopsis</i>), outperformed two tested state-of-the-art algorithms by mapping accuracy and computation time.</p></div

    Comparative effectiveness of the initial solutions and thelocal search procedures on the simulated problems.

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    <p>The utilization of the initial solution step (column 4) and the local search (column 5) considerably reduces the computation time on the test problem.</p><p><sup>1</sup> The three mutation procedures are working.</p><p><sup>2</sup> The Initial solution used.</p><p><sup>3</sup> The local search used.</p><p>Comparative effectiveness of the initial solutions and thelocal search procedures on the simulated problems.</p
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