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An alternative to using our heuristic that may recapture some lost variants that were slow to converge in informative samples would be to use diagnostics for information loss based on the observed-data observed information matrix such as condition number or the spectral decomposition approach suggested by Meng and Rubin (1991).
This is so, at least in part, because concern is about classification accuracy, and obtaining more informative samples for each class is more important than avoiding loss of information resulting from discarding some sire means.
Although these facts, the entropy- based sampling select informative samples.
AL queries labels of most informative samples and maximizes gain from the labeling effort.
Despite this, remarkably few studies in the social sciences use genetically informative samples.
Focus is on how to select the most highly informative samples for effectively training the neural architecture.
Most of the studies, which have been carried out in genetically informative samples, lack methodological consistency with regard to frequency and timing of sample collection.
Strategies of sample proposal: In order to use informative samples for training a scene-specialized classifier, we put forward two sample-proposal strategies.
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IHC staining of such non-informative samples were replaced and performed by using whole tissue slides.
After eliminating non-informative samples and samples with RNA of insufficient quality 109 cases and 125 controls were studied.
Non-informative samples included lost samples, unrepresentative samples, samples with too few tumor cells, and samples with inappropriate staining; such were not used in data complication.
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CEO of Professional Science Editing for Scientists @ prosciediting.com