The interpolation method, the tectonic block model, and traditional coordinate transformation were used to analyze the accuracy in estimating and predicting the coordinate changes at arbitrary sites.
The purpose of this paper is to provide a horizontal velocity model based on 265 GPS observations for estimating and predicting the coordinate changes associated with the horizontal crustal motion in SW Taiwan (Fig. 1).
We performed 1,200 splits of data, where in each split we constructed a test set consisting of one individual and the remaining individuals were used as a training set in order to select PCAIMs and predict the coordinates of the test set sample. Figure 1 and Table 2 summarize the performance of our PCAIM panels over all 1,200 individuals in all test sets.
It can estimate the state of the dynamic system using a series of measurements containing noise and other inaccuracies and predict the coordinate position and velocity of the object according to the observation sequence of the noise.
We then used the training set individuals to compute panels of AIMs of various sizes (PCAIMs with redundancy removal) and then we employed our NN algorithm in order to predict the coordinates of origin of the test set individual.
However, if one wants to consider pulses that arrive before the trajectory relaxes back to the limit cycle, one needs to compute also the amplitude displacement in order to predict the coordinates of the point at the next stimulation time.
When considering CONTRA results (Additional file 1: Table S1), it was observed that CONTRA predicts the coordinates of each exon with copy number alteration while all the other programs predict CNVs with one or multiple exons.
This tool parses the structure coordinate files, removes the hetero atoms and water molecules, and predicts the interaction based on coordinate distance between atoms of amino acid residues of small peptide and the protein.
ZPRED and ZPRED2 predict the Z-coordinate, i.e. the distance from the membrane center to a residue, for α-helical membrane proteins (Granseth et al., 2006; Papaloukas et al., 2008) using artificial neural networks.
These data were subjected to the zero-shot decoding scheme and used to predict the semantic coordinates of the 60 items.
