SwePub
Sök i SwePub databas

  Utökad sökning

Träfflista för sökning "L773:0219 7200 OR L773:1757 6334 srt2:(2010-2014)"

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

  • Resultat 1-6 av 6
Sortera/gruppera träfflistan
   
NumreringReferensOmslagsbildHitta
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.
  •  
2.
  • Deo, Ameya, et al. (författare)
  • How to Choose a Normalization Strategy for miRNA Quantitative Real-Time (qPCR) Arrays
  • 2011
  • Ingår i: Journal of Bioinformatics and Computational Biology. - : Imperial College Press. - 0219-7200 .- 1757-6334. ; 9:6, s. 795-812
  • Tidskriftsartikel (refereegranskat)abstract
    • Low-density arrays for quantitative real-time PCR (qPCR) are increasingly being used as an experimental technique for miRNA expression profiling. As with gene expression profiling using microarrays, data from such experiments needs effective analysis methods to produce reliable and high-quality results. In the pre-processing of the data, one crucial analysis step is normalization, which aims to reduce measurement errors and technical variability among arrays that might have arisen during the execution of the experiments. However, there are currently a number of different approaches to choose among and an unsuitable applied method may induce misleading effects, which could affect the subsequent analysis steps and thereby any conclusions drawn from the results. The choice of normalization method is hence an important issue to consider. In this study we present the comparison of a number of data-driven normalization methods for TaqMan low-density arrays for qPCR and different descriptive statistical techniques that can facilitate the choice of normalization method. The performance of the normalization methods was assessed and compared against each other as well as against standard normalization using endogenous controls. The results clearly show that the data-driven methods reduce variation and represent robust alternatives to using endogenous controls.
  •  
3.
  • Gennemark, Peter, 1974, et al. (författare)
  • ODEion- a software module for structural identification of ordinary differential equations
  • 2014
  • Ingår i: Journal of Bioinformatics and Computational Biology. - 0219-7200 .- 1757-6334. ; 12:1
  • Tidskriftsartikel (refereegranskat)abstract
    • In the systems biology field, algorithms for structural identification of ordinary differential equations (ODEs) have mainly focused on fixed model spaces like S-systems and/or on methods that require sufficiently good data so that derivatives can be accurately estimated. There is therefore a lack of methods and software that can handle more general models and realistic data. We present ODEion, a software module for structural identification of ODEs. Main characteristic features of the software are: • The model space is defined by arbitrary user-defined functions that can be nonlinear in both variables and parameters, such as for example chemical rate reactions. • ODEion implements computationally efficient algorithms that have been shown to efficiently handle sparse and noisy data. It can run a range of realistic problems that previously required a supercomputer. • ODEion is easy to use and provides SBML output. We describe the mathematical problem, the ODEion system itself, and provide several examples of how the system can be used. Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219720013500157
  •  
4.
  • 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.
  •  
5.
  • Ma, Zhanyu, 1982-, et al. (författare)
  • A variational bayes beta mixture model for feature selection in DNA methylation studies
  • 2013
  • Ingår i: Journal of Bioinformatics and Computational Biology. - 0219-7200 .- 1757-6334. ; 11:4, s. 1350005-
  • Tidskriftsartikel (refereegranskat)abstract
    • An increasing number of studies are using beadarrays to measure DNA methylation on a genome-wide basis. The purpose is to identify novel biomarkers in a wide range of complex genetic diseases including cancer. A common difficulty encountered in these studies is distinguishing true biomarkers from false positives. While statistical methods aimed at improving the feature selection step have been developed for gene expression, relatively few methods have been adapted to DNA methylation data, which is naturally beta-distributed. Here we explore and propose an innovative application of a recently developed variational Bayesian beta-mixture model (VBBMM) to the feature selection problem in the context of DNA methylation data generated from a highly popular beadarray technology. We demonstrate that VBBMM offers significant improvements in inference and feature selection in this type of data compared to an Expectation-Maximization (EM) algorithm, at a significantly reduced computational cost. We further demonstrate the added value of VBBMM as a feature selection and prioritization step in the context of identifying prognostic markers in breast cancer. A variational Bayesian approach to feature selection of DNA methylation profiles should thus be of value to any study undergoing large-scale DNA methylation profiling in search of novel biomarkers.
  •  
6.
  • Kuhn, C., et al. (författare)
  • Modeling yeast osmoadaptation at different levels of resolution
  • 2013
  • Ingår i: Journal of Bioinformatics and Computational Biology. - 0219-7200. ; 11:2
  • Tidskriftsartikel (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.
  •  
Skapa referenser, mejla, bekava och länka
  • Resultat 1-6 av 6

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy