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MedianCD normalization identified 2,090 no-variation genes, of which 95.4% were known, and among which 98.7% were known negatives.
MedianCD normalization identified 561 no-variation genes, of which 93.9% were known, and among which 84.1% were known negatives.
The probe set-based analysis using RMA normalization identified 10,698 probe sets (48%) expressed above background.
This additional normalization identified further changes that were not apparent by standard peak identification.
The spike-based normalization identified 100% of the inducible genes as expected.
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Bio-entity identification and normalization: identifying mentions of relevant bio-entities and linking the entities to database identifiers.
This crucial initial task is required in order to perform other information extraction steps, such as identifying the exact concepts associated with the entity names mentioned in the texts (a task called entity normalization), identifying relations and events involving those concepts, or inferring concept characteristics and other associations [1].
In contrast, the median normalization algorithm identified only 13% inducible genes because it assumes a non-existing normal distribution of the data.
We performed plate-wise quantile normalization, then identified CNVs for each plate using each of the four programs.
After normalization we identified structural variation from both dye bias and experimental set using principal components analysis (PCA, data not shown).
Using the method described above for temporal cycle normalization, we identified EMG, kinematic and dynamic signals for averaging and statistical analysis.
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CEO of Professional Science Editing for Scientists @ prosciediting.com