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We proposed a generic idea that involves employing these algorithms as part of a pipeline in which the training data gathering procedure and the training process are automated.
In the remainder of this paper, we define a generic pipeline for the purpose of ApicoTP prediction (hereafter referred to as the ApicoAP pipeline) that consists of an automated training data gathering procedure and the ApicoTP classifier training routine.
By defining these algorithms in a pipeline in which the training data gathering procedure and the learning process are automated, one can create a system that generates a classifier or predictor using information available from public resources.
For this case study, we selected the apicoplast-targeted protein prediction problem and utilized the existing machine learning model that we developed previously, ApicoAP [ 6], in a pipeline comprised of an automated training data gathering procedure and the classifier training routine defined as part of the ApicoAP model.
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There is a clear need for more consistent data collection protocols and for systematic studies of the effects of variability in data gathering procedures and data collection effort on observed network structure.
In [15], a CS-based distributed communication architecture is exploited to minimize the latency for information retrieval under a favorable power-distortion trade-off, whereas in [16], a CS-based sensing and data gathering procedure is analyzed for the case of network routing tree topologies.
We believe that applications that learn from genomic data should be defined in a pipeline in which the data gathering procedure for training data is automated and the learning process is as well.
The data gathering procedure is representative for painting specific analysis performed in museum studios.
We now turn to quantifying the energy consumption and bandwidth occupancy required for realizing the Radon-like CS by means of a data gathering procedure in a WSN.
As we will show in the following, this quadrant-based approach may be used to derive a specific data gathering procedure.
To recap, the Radon-like CS data gathering procedure lets the fusion center collect all the needed measurements in a highly parallelized fashion.
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