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Most works in network sampling have focused on the problem of estimating how the network measures will change in the sample given a particular sampling scheme such as a snowball, random sampling of nodes and links, or random walk [13], [21], [22].
These statistical learning algorithms compute the probability of observing the specific data associated with a sample given a particular experimental group.
Srinivasan et al. [ 14] developed an alignment-free n-gram-based method named MetaID that can accurately identify microorganisms at the strain level and estimate the abundance of each organism in a sample given a metagenomic sequencing dataset.
Here, we present MetaID, an alignment-free n-gram based approach that can accurately identify microorganisms at the strain level and estimate the abundance of each organism in a sample, given a metagenomic sequencing dataset.
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We also report sensitivity, ie, the probability of an HPV-positive urine sample given an HPV-positive cervical sample, and specificity, ie, the probability an HPV-negative urine sample given an HPV-negative cervical sample.
The Guardian's Ian Sample gives a gripping account of the hunt.
This geodesics sample give a dynamics-based smoothing prior on the state transition space.
The umTS1 sample gives a much lower conversion and lower selectivity of dihydroxylbenzenes (DHB) than the nmTS1 sample.
The resulting sample gave a SEM image of the swollen seaweed whose thickness was several times larger than dried one.
The concentration of the sample giving a signal-to-noise ratio of ten was fixed as LOQ.
The concentration of sample giving a signal-to-noise ratio of three was fixed as the LOD.
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