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Using the BAC end sequences and physical map that are available for the rice blast fungus, Magnaporthe grisea, we describe a likelihood ratio test designed to identify clustering of TEs in the genome.
We describe a likelihood based methodology for estimating the reproductive number at each day in the epidemic as well as the serial interval.
Here, we describe a likelihood model in which the number of chromosomes in a genome evolves according to a Markov process with one rate of chromosome duplication and loss that is proportional to the number of chromosomes in the genome and another stochastic rate at which every chromosome in the genome could duplicate in a single event.
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Also in [24], Su et al. described a likelihood test-based modulation classification method for identifying the modulation scheme of SDR in real-time without pilot transmission.
We describe a simple likelihood of the marker data conditional on the trait data for a sample of affected sib pairs, with disease penetrances and disease-SNP haplotype frequencies as parameters.
In this talk I will describe a multicomponent likelihood analysis framework for joint analyses of various CMB datasets that takes into account multiple signal types, and its subsequent use for analysis of BICEP2, Keck and Planck CMB polarization data to derive the tightest current constraints, parametrized by the tensor-to-scalar ratio, on primordial B-modes (r<0.07).
I describe a maximum likelihood method for joint estimation of ancestry and interclass heterozygosity.
Ettinger et al. also describe a higher likelihood of disabilities in OA patients with heart diseases [ 28].
Bessell et al. [ 10] describe a higher likelihood of reporting infection in areas of lower deprivation and lower population density.
We describe a maximum likelihood approach to estimate rates and times of spacer insertions and deletions, given a set of ordered spacer arrays from different strains.
Here, we describe a new likelihood ratio test that is an extension of the method of Stekel et al. 2000 [ 15], that instead of identifying differentially expressed genes across a set of different libraries, will identify genes that are differentially expressed between two groups of libraries; within the groups the libraries should be the same, but between groups they may be different.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com