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Thus to conduct the selection of informative motifs, we have elected to utilize the results from the parametric evaluation and select an AB of 3 for the corticosteroid datasets, and an AB of 4 for the circadian and burn datasets.
Under all cases, the corticosteroid and the burn datasets exhibit a much greater deviation from the underlying exponential distribution than the circadian and random datasets, as expressed by the R2 value of the corresponding fit to an exponential.
Furthermore, it was observed that by setting the AB parameter to 3, one is able to maximize the deviation away from the exponential distribution for the corticosteroid datasets, whereas an AB parameter of 4 maximized the deviation away from the exponential distribution for the circadian dataset and the burn datasets.
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Burn, burn, burn!
The burned area was highly correlated (R2 = 0.93) with a high resolution imagery reference burn scar dataset, but exhibited a large over estimation of burned area (56%).
The reference burn scar dataset was used to calibrate the algorithm response and quantify the uncertainty in the burned area measurement at the fire incident level.
The burn injury dataset (GDS599) yielded 4 clusters with 281 probes under the greedy selection (Figure 4c) and 5 clusters with 307 genes under the optimal selection (Figure 4d).
Burn, bits, burn.
This dataset represents a serious cutaneous burn administered to a rat over 30 percent of the skin.
They burn!
"Burn me".
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