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This is not surprising, as it has long been recognized that multi-component approaches to prediction achieve the best performance.
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The classifier for kidney-selective gene prediction achieved predictive performance with overall accuracy at 93.62% with MCC = 0.4648 and ROC AUC = 0.9300.
It is noteworthy that the classifiers used for tissue-specific gene prediction achieved higher predictive performance than those for tissue-selective gene prediction.
However, even if these actions did contain sufficient cues for infants to interpret them as goal-directed, there are still reasons to question the assumption that predictive eye movements reflect a goal prediction achieved by a motor process.
We noted that predictive ability delivered by all markers was close to the best prediction achieved by the individual genomic regions.
Simulation results revealed that on-line adaptive seizure prediction achieves better results than off-line non-adaptive seizure prediction.
Finally, we show that the DFBA based on LTE KPI prediction achieves in average performance improvements of 33%% over approaches involving simpler SINR-based models.
This is in contrast to the successful class comparison and prediction achieved with a publicly available cancer dataset [5].
The level of prediction achieved using just two biological traits was 0.79 for naturalized, 0.75 for weeds, and 0.82 for noxious.
SMO classifier based prediction achieved sensitivity of 0.422 and 0.388 MCC (Additional file 1: Figure S7).
Our final prediction achieved the best performance among all seven teams that participated in this sub-chellenge.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com