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To understand the genes regulated by ZNF217 and the pathways associated with ERα function, we performed gene ontology analysis on the lists of differentially regulated genes following ZNF217 silencing with the GREAT classification gene ontology tool [ 16].
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This in turn is expected to provide greater classification performance.
Distinguishing between normal versus bulged disks demonstrated the greatest classification disagreement.
The experimental results showed that the proposed method achieves greater classification accuracy than other vector- and tensor-based methods.
The three-class solution had the highest entropy estimate (0.633 vs. 0.569 for the 3- and 4-class models, respectively), suggesting greater classification uncertainty with the extraction of one additional class.
Greater classification depth was achieved with a complementary transmembrane-focused support vector machine (SVM -based claSVM -based
In contrast, in classifications using local "searchlights" and a whole brain analysis, we find greater classification accuracy in posterior rather than anterior temporal regions.
In the Transcription (11) functional category in particular this facet seems to allow for a greater classification precision to be achieved across all three datasets.
Misclassification was differential in our data set, as those closest to the highway had the greatest classification error, attributable in part to street network geocoding [ 15].
While using image volumes with two such contrasts does result in greater classification accuracy and less sensitivity to intensity bias shading, it also increases scan time.
The left posterior and right mid-STS showed the greatest classification accuracy in discriminations of intelligibility when they were expressed relative to the accuracy in discriminations of spectral detail.
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CEO of Professional Science Editing for Scientists @ prosciediting.com