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Yet, comparative evaluations revealed that state-of-the-art matchers fall short of offering high performance across varied datasets.
Current research challenges in earth science rely on a deeper understanding of how to integrate these varied datasets and their associated data uncertainty.
Availability: MolBioLib is available for download at: http://sourceforge.net/projects/molbiolib Contact: [email protected] Next-generation sequencing requires a data analysis approach capable of handling large, complex and varied datasets, from large sets of reads to complex polymorphisms to existing feature files.
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We used extensive data simulations to (1) systematically vary dataset size, branch length, and tree complexity, and (2) compare estimated branch lengths to known branch lengths to identify the determinants of estimation accuracy in both Bayesian and maximum likelihood frameworks.
Note, since the outcomes are simulated, they vary from dataset to dataset; as such, the observed actual phase I stratification varied from dataset to dataset.
It is observed that the performance of these techniques varied from dataset to dataset, which make them inconsistent for fault prediction in the unknown software project.
Noise of unexpressed genes varied between datasets, indicating either a tissue-specific effect or an artefact of the preprocessing of each dataset.
Several observations can be made from Figure 2. First, the classification accuracy varied across datasets, which reflects the relative subtlety in the biological differences between two classes in each dataset.
Accuracy varied between datasets where the median difference between true and estimated geographic locations ranged from 192 to 902 km.
Recording intervals and survey methods (e.g., line-transect or opportunistic survey) varied among datasets.
||# of snps and/or genes varied among datasets in this paper.
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