As we can observe for both descriptors, the more users are considered, the more effective are the results (better precision × recall curves).
Whereas AMPs present high in vivo and very low in vitro aggregation propensity, non-AMPs can be divided in two main groups, presenting either high or low values for both descriptors.
Survivors and non-survivors median scores for both descriptors were compared using Mann–Whitney U-test; the relative risk (RR) was also calculated.
Based on destructive measurements, estimates of both descriptors for a two-row 31-year-old green ash (Fraxinus pennsylvanica Marsh).
This paper proposes a novel fault estimation observer for both nonlinear descriptor and normal systems, which are subjected simultaneously to actuator faults, sensor faults and unstructured non-parametric uncertainties.
After model assessment for both analyses (descriptors and triggers), three-class models were determined to have the best fit for these data.
The findings from this research are based on an exploratory analysis with relatively complex models for both the descriptors and the triggers.
Despite this, upon qualitative assessment, the classes for both the descriptors and the triggers were unable to be meaningfully labeled based on the anatomical derivation of the dizziness (central, peripheral vestibular, and cervical) as hypothesized.
The topology of the neural network is the same for both sets of descriptors, in order to allow comparison of the discriminative power of the two feature sets.
In Shen et al.'s algorithm, for every descriptor there are both positive and negative weights.
