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In terms of the functional coherence of the detected modules, the combined method performs far better than sequence-based alignment.
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We show that other known methods perform far worse and have much more variability in their accuracy.
We show that other known methods perform far worse and have much more variability in their accuracy, both across anatomical components and between their sensitivity (i.e. the ability to correctly classify images that contain gene expression) and specificity (i.e. the ability to correctly classify images that do not contain gene expression).
So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools.
When CWS is present (dataset 2), the proposed method performs better than the others with slightly lower delay and much lower FAR.
Although this method performs relatively better for a channel with a large delay spread, its BER performance is still far away from the perfect channel estimation.
Our method performs generally better than Reddy and Raj's across a range of SNRs, especially when SNR is far away from 0 dB.
Cox1 is classified as an exception as no method perform well on this target: ACPC, Pharao and Shape-it all have median AUCs below 0.5 and MACCS has a huge standard deviation with a mean not far from 0.5.
However, GIFT perform far better than baseline methods in all cases.
In terms of stability, QMOM and EQMOM performed far better than the Maximum Entropy method.
This 11 taxon phylogeny combines elements of the balanced and pectinate topologies performed far better than either the NJ with Euclidean distance or parsimony methods.
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