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Hydroelectric Power Tags > Tag based links for Accuracy

The following links have been tagged accuracy by users just like you, because these resources are off-site we cannot guarantee the accuracy or quality of any third-party information.

1. Accuracy of structure-base d sequence alignments of automatic methods: BMC Bioinformatics , Vol. 8 (20 September 2007), 355.
   
   Source: BMC Bioinformatics, Vol. 8 (20 September 2007), 355.
   
   
2. Measures of Clade Confidence Do Not Correlate with Accuracy of Phylogenetic Trees: PLoS Computational Biology, Vol. 3, No. 3. (1 March 2007), e51.Metrics of phylogenetic tree reliability, such as parametric bootstrap percentages or Bayesian posterior probabilities, represent internal measures of the topological reproducibilit y of a phylogenetic tree, while the recently introduced aLRT (approximate likelihood ratio test) assesses the likelihood that a branch exists on a maximum-likeli hood tree. Although those values are often equated with phylogenetic tree accuracy, they do not necessarily estimate how well a reconstructed phylogeny represents cladistic relationships that actually exist in nature. The authors have therefore attempted to quantify how well bootstrap percentages, posterior probabilities, and aLRT measures reflect the probability that a deduced phylogenetic clade is present in a known phylogeny. The authors simulated the evolution of bacterial genes of varying lengths under biologically realistic conditions, and reconstructed those known phylogenies using both maximum likelihood and Bayesian methods. Then, they measured how frequently clades in the reconstructed trees exhibiting particular bootstrap percentages, aLRT values, or posterior probabilities were found in the true trees. The authors have observed that none of these values correlate with the probability that a given clade is present in the known phylogeny. The major conclusion is that none of the measures provide any information about the likelihood that an individual clade actually exists. It is also found that the mean of all clade support values on a tree closely reflects the average proportion of all clades that have been assigned correctly, and is thus a good representation of the overall accuracy of a phylogenetic tree.
   
   Source: PLoS Computational Biology, Vol. 3, No. 3. (1 March 2007), e51.
   
   
3. Genetic markers: How accurate can genetic data be?: Heredity, Vol. aop, No. current.
   
   Source: Heredity, Vol. aop, No. current.
   
   
4. A taxonomy of indoor and outdoor positioning techniques for mobile location services: SIGecom Exch., Vol. 3, No. 4. (2003), pp. 19-27.
   
   Source: SIGecom Exch., Vol. 3, No. 4. (2003), pp. 19-27.
   
   
5. State of the art: refinement of multiple sequence alignments: BMC Bioinformatics , Vol. 7 (14 November 2006), 499.
   
   Source: BMC Bioinformatics, Vol. 7 (14 November 2006), 499.
   
   
6. The effect of the guide tree on multiple sequence alignments and subsequent phylogenetic analyses.: Pac Symp Biocomput (2008), pp. 25-36.Many multiple sequence alignment methods (MSAs) use guide trees in conjunction with a progressive alignment technique to generate a multiple sequence alignment but use differing techniques to produce the guide tree and to perform the progressive alignment. In this paper we explore the consequences of changing the guide tree used for the alignment routine. We evaluate four leading MSA methods (ProbCons, MAFFT, Muscle, and ClustalW) as well as a new MSA method (FTA, for "Fixed Tree Alignment") which we have developed, on a wide range of simulated datasets. Although improvements in alignment accuracy can be obtained by providing better guide trees, in general there is little effect on the "accuracy" (measured using the SP-score) of the alignment by improving the guide tree. However, RAxML-based phylogenetic analyses of alignments based upon better guide trees tend to be much more accurate. This impact is particularly significant for ProbCons, one of the best MSA methods currently available, and our method, FTA. Finally, for very good guide trees, phylogenies based upon FTA alignments are more accurate than phylogenies based upon ProbCons alignments, suggesting that further improvements in phylogenetic accuracy may be obtained through algorithms of this type.
   
   Source: Pac Symp Biocomput (2008), pp. 25-36.
   
   
7. Barking Up The Wrong Treelength: The Impact of Gap Penalty on Alignment and Tree Accuracy: IEEE/ACM Transactions on Computational Biology and Bioinformatics , Vol. 99, No. 1. (5555)Several methods have been developed for simultaneous estimation of alignment and tree, of which POY is the most popular. In a 2007 paper published in Systematic Biology, Ogden and Rosenberg reported on a simulation study in which they compared POY to estimating the alignment using ClustalW and then analyzing the resultant alignment using maximum parsimony. They found that ClustalW+MP outperformed POY with respect to alignment and phylogenetic tree accuracy, and they concluded that simultaneous estimation techniques are not competitive with two-phase techniques. Our paper presents a simulation study in which we focus on the NP-hard optimization problem that POY addresses: minimizing treelength. Our study considers the impact of the gap penalty and suggests that the poor performance observed for POY by Ogden and Rosenberg is due to the simple gap penalties they used to score alignment/tree pairs. Our study suggests that optimizing under an affine gap penalty might produce alignments that are better than ClustalW alignments, and competitive with those produced by the best current alignment methods. We also show that optimizing under this affine gap penalty produces trees whose topological accuracy is better than ClustalW+MP, and competitive with the current best two-phase methods.
   
   Source: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 99, No. 1. (5555)
   
   
8. Alignment Uncertainty and Genomic Analysis: Science, Vol. 319, No. 5862. (25 January 2008), pp. 473-476.The statistical methods applied to the analysis of genomic data do not account for uncertainty in the sequence alignment. Indeed, the alignment is treated as an observation, and all of the subsequent inferences depend on the alignment being correct. This may not have been too problematic for many phylogenetic studies, in which the gene is carefully chosen for, among other things, ease of alignment. However, in a comparative genomics study, the same statistical methods are applied repeatedly on thousands of genes, many of which will be difficult to align. Using genomic data from seven yeast species, we show that uncertainty in the alignment can lead to several problems, including different alignment methods resulting in different conclusions. 10.1126/scienc e.1151532
   
   Source: Science, Vol. 319, No. 5862. (25 January 2008), pp. 473-476.
   
   
9. Bayesian coestimation of phylogeny and sequence alignment.: BMC Bioinformatics , Vol. 6 (2005)BACKGROU ND: Two central problems in computational biology are the determination of the alignment and phylogeny of a set of biological sequences. The traditional approach to this problem is to first build a multiple alignment of these sequences, followed by a phylogenetic reconstruction step based on this multiple alignment. However, alignment and phylogenetic inference are fundamentally interdependent , and ignoring this fact leads to biased and overconfident estimations. Whether the main interest be in sequence alignment or phylogeny, a major goal of computational biology is the co-estimation of both. RESULTS: We developed a fully Bayesian Markov chain Monte Carlo method for coestimating phylogeny and sequence alignment, under the Thorne-Kishino -Felsenstein model of substitution and single nucleotide insertion-dele tion (indel) events. In our earlier work, we introduced a novel and efficient algorithm, termed the "indel peeling algorithm", which includes indels as phylogenetical ly informative evolutionary events, and resembles Felsenstein's peeling algorithm for substitutions on a phylogenetic tree. For a fixed alignment, our extension analytically integrates out both substitution and indel events within a proper statistical model, without the need for data augmentation at internal tree nodes, allowing for efficient sampling of tree topologies and edge lengths. To additionally sample multiple alignments, we here introduce an efficient partial Metropolized independence sampler for alignments, and combine these two algorithms into a fully Bayesian co-estimation procedure for the alignment and phylogeny problem. Our approach results in estimates for the posterior distribution of evolutionary rate parameters, for the maximum a-posteriori (MAP) phylogenetic tree, and for the posterior decoding alignment. Estimates for the evolutionary tree and multiple alignment are augmented with confidence estimates for each node height and alignment column. Our results indicate that the patterns in reliability broadly correspond to structural features of the proteins, and thus provides biologically meaningful information which is not existent in the usual point-estimate of the alignment. Our methods can handle input data of moderate size (10-20 protein sequences, each 100-200 bp), which we analyzed overnight on a standard 2 GHz personal computer. CONCLUSION: Joint analysis of multiple sequence alignment, evolutionary trees and additional evolutionary parameters can be now done within a single coherent statistical framework.
   
   Source: BMC Bioinformatics, Vol. 6 (2005)
   
   
10. Measuring global credibility with application to local sequence alignment.: PLoS computational biology, Vol. 4, No. 5. (May 2008)Computati onal biology is replete with high-dimension al (high-D) discrete prediction and inference problems, including sequence alignment, RNA structure prediction, phylogenetic inference, motif finding, prediction of pathways, and model selection problems in statistical genetics. Even though prediction and inference in these settings are uncertain, little attention has been focused on the development of global measures of uncertainty. Regardless of the procedure employed to produce a prediction, when a procedure delivers a single answer, that answer is a point estimate selected from the solution ensemble, the set of all possible solutions. For high-D discrete space, these ensembles are immense, and thus there is considerable uncertainty. We recommend the use of Bayesian credibility limits to describe this uncertainty, where a (1-alpha)%, 0
    
    Source: PLoS computational biology, Vol. 4, No. 5. (May 2008)
    
    

If you would like to find additional social bookmark based links on the topic of accuracy we recommend the Open Tag Directory > Accuracy. If you would like to find related tags we recommend Tag Patterns > Accuracy.

       

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