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The proposed algorithm demonstrates the feasibility of a novel training approach based on optimized datasets created directly in-situ using images taken by the spacecraft on-board camera.
Our methods were either developed specifically for Tara Oceans samples or carefully selected among existing ones in order to meet the requirements of our sequencing strategy and to produce optimized datasets for downstream bioinformatics analyses, as for example the production of overlapping reads from metagenomics libraries to facilitate assembly.
Because the sequences within these optimized datasets are no longer randomly sampled within each species, a possible concern is that our results are not representative of random samples.
However, as the strategies applied to each of these optimized datasets retain their general relationships across datasets (e.g., those that are topologically-based and those that are not strictly topologically-based), the conclusions drawn in this article should also hold for large noncoding datasets.
It is from dataset that we create the four optimized datasets as summarized in Table 2 and Figure 2. The first dataset, D s, consists of alignments with a single individual sampled per species at each locus (not necessarily the same individual across loci).
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Optimized dataset of all molecules was considered for GQSAR analysis on the basis of common DHP template.
Although such work is of importance, the discussion requires a new focus on real and non-optimized image datasets in order to draw conclusions for actual measurements.
The accuracy obtained with these methods could probably be increased if the choice of parameters was optimized for these datasets.
Our efforts will establish a new baseline dataset, optimized to directly accelerate development of modern, intelligent, automated analysis of underwater visual data.
This paper describes the generation of a regionally optimized land cover dataset for West Africa with a spatial resolution of 250 m, which is based on earth observation data from three optical and radar instruments.
The parameters of the reference descriptors GF and GLCM are also optimized for each dataset.
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