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Our approach leverages information from next-generation sequence data for multiple individuals by using the Expectation-Maximization (EM) algorithm to numerically maximize the observed data likelihood with respect to genotype frequencies and the nucleotide-read error rate.
Because we maximize the observed data likelihood using the EM algorithm to estimate parameters, we call this approach SeqEM.
In small samples, we observed that for some variant sites the EM algorithm converged very slowly or converged to unrealistic parameter values because of lack of identifiability (i.e. more than one set of parameter values maximize the observed data likelihood).
Given RNA-Seq data, we estimate expression levels by finding the values of θ that maximize the observed data likelihood: (3) Equation (3) shows that our goal is to find the ML proportions of a mixture model.
The microwave frequency is tuned to maximize the observed fluorescence, which occurs at the natural resonance frequency (9,192,631,770 Hz) of the cesium atom.
Furthermore, our results show that communities which wanted to claim land tenure over a larger territory use burial sites locations in order to maximize the observed area within habitation sites surroundings.
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The 4 h end point was chosen in order to maximize the potential observed adduction and to avoid complication of the analysis by biological degradation of adducted protein.
Testing samples collected less than 5 days after onset of illness maximized the proportion observed to seroconvert, although the cut-off point for acute sera was less critical.
The fluorophores were chosen to maximize the fluorescence changes observed upon moving from an aqueous environment (low fluorescence intensity) to an hydrophobic environment such as a protein's binding site (high fluorescence intensity).
We describe a general optimization procedure used to maximize the correlation between the observed and simulated spatio-temporal response of a pulsed single element in a commercial ultrasound probe.
The goal of EM clustering is to estimate the means and standard deviations for each cluster so as to maximize the likelihood of the observed data.
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