Exact(1)
Previous work has systematically explored the inference of triple quadrupole MS/MS fragmentation from ion trap MS/MS data and from HCD data.
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In this paper, we demonstrate that segregation analysis is an old solution to a new problem, by applying a segregation analysis method to simulated data to explore the inference of copy number alleles at the Agouti locus in sheep.
This study explored the application of statistical inference to dissect causal relationships at complex-trait loci where there is a concomitant association with one or more metabolites.
The network itself can be thought of as a simple qualitative model in its own right, and many investigators have explored the problem of network inference from gene expression data (for a review see Hecker et al. 2009 and references therein).
Recently, Chen and Liu [Chen, J., Liu, M.Q., 2008a. Optimal mixed-level k-circulant supersaturated designs. J. Statist. Plann. Inference 138, 4151 4157] explored the construction of E fNOD -optimal mixE fNOD -optimalsing k-cyclic generators.
Prill et al [ 27] further explored the issue of intrinsic impediments to network inference, designating identifiability of certain network edges and systematic false positives as the main barriers.
By combining structural and sequence analysis, and phylogenetic inference, we have explored the evolutionary history of classes A, B, E, F, and G and demonstrate that their multidomain architectures reflect their phylogenetic relationships, suggesting that the main evolutionary steps in their divergence are likely to have arisen from the recruitment of different domains.
Rather, the inference of tree topology is explored much more often, where the judicious choice and use of underlying models, optimality criteria, branch support measures, etc. are a mandatory consideration in virtually all publications and the potentially different outcomes are discussed critically.
As a demonstration, we explored the properties of several domain families and used the NovelFam3000 system to develop data-based inferences.
We explored the potential impact of compositional heterogeneity on our inference by using the Dayhoff recoding, an approach known to be efficient [ 37- 39].
This 'selection-based' approach explores the robustness of inference under local departures from the MAR assumption, meaning that the sensitivity to departure from the MAR assumption can be calculated from the observed data without estimating a full non-ignorable model [ 21].
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