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One way of testing for randomness in the distribution of gold particles is to construct the random (expected) distribution and then compare this to the observed distribution.
In addition, three types of fuzzy random expected value models are presented to model fuzzy random decision systems.
Moreover, a hybrid intelligent algorithm, which incorporates simulation, neural network and genetic algorithm, is designed in order to solve general fuzzy random expected value models.
(B ) Relative abundance of particular amino acids in proteins (random expected: black, all CDSs: purple, all annotated smORFs: yellow, and translated smORFs: red).
Using a list of genomic regions and two lists of features mapped to them, this method provides a z-score corresponding to the number of standard deviations of the observed overlap compared to the random expected overlap.
To derive the lod score of an amino acid, the logarithm of the ratio of the observed frequency of a pair of amino acidsis divided by the random expected frequency of the same pair of amino acids.
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As in the inbred lines, the latter two mutation types were considerably depleted in favor of synonymous SNPs compared with random expectation, as expected under purifying selection (44.1% missense mutations vs. 70.8% expected at random and 0.56% nonsense mutations vs. 4.4% expected at random, respectively, binomial test P < 1 × 10−300 in both cases).
ANNE Random is expected to be significantly inferior to all other models, in agreement with the results seen with the training sets.
The second term denotes the fraction of random PETs expected not to overlap with other fragments [5].
Using the random model expected three-phrase combination frequencies were calculated as the product of the frequency of each phrase.
Therefore, missing was not completely at random as expected.
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