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Sökning: L773:0219 7200 OR L773:1757 6334 > (2010-2014) > Uppsala universitet

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1.
  • Akhtar, Malik N., et al. (författare)
  • Identification of best indicators of peptide-spectrum match using a permutation resampling approach
  • 2014
  • Ingår i: Journal of Bioinformatics and Computational Biology. - 0219-7200 .- 1757-6334. ; 12:5, s. 1440001-
  • Tidskriftsartikel (refereegranskat)abstract
    • Various indicators of observed-theoretical spectrum matches were compared and the resulting statistical significance was characterized using permutation resampling. Novel decoy databases built by resampling the terminal positions of peptide sequences were evaluated to identify the conditions for accurate computation of peptide match significance levels. The methodology was tested on real and manually curated tandem mass spectra from peptides across a wide range of sizes. Spectra match indicators from complementary database search programs were pro filed and optimal indicators were identified. The combination of the optimal indicator and permuted decoy databases improved the calculation of the peptide match significance compared to the approaches currently implemented in the database search programs that rely on distributional assumptions. Permutation tests using p-values obtained from software-dependent matching scores and E-values outperformed permutation tests using all other indicators. The higher overlap in matches between the database search programs when using end permutation compared to existing approaches con firmed the superiority of the end permutation method to identify peptides. The combination of effective match indicators and the end permutation method is recommended for accurate detection of peptides.
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2.
  • Kühn, Clemens, et al. (författare)
  • Modeling yeast osmoadaptation at different levels of resolution
  • 2013
  • Ingår i: Journal of Bioinformatics and Computational Biology. - 0219-7200 .- 1757-6334. ; 11:2, s. 1330001-
  • Forskningsöversikt (refereegranskat)abstract
    • We review the proposed mathematical models of the response to osmotic stress in yeast. These models mainly differ in the choice of mathematical representation (e. g. Bayesian networks, ordinary differential equations, or rule-based models), the extent to which the modeling is data-driven, and predictability. The overview exemplifies how one biological system can be modeled with various modeling techniques and at different levels of resolution, and how the choice typically is based on the amount and quality of available data, prior information of the system, and the research question in focus. As a natural part of the overview, we discuss requirements, advantages, and limitations of the different modeling approaches.
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