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As demonstrated by our results, generalization to independent datasets in the presence of high levels of confounding factors (such as clinical site, exact composition of trial population, etc). is very difficult and research should be invested to not only find better mathematical approaches to exploit the dataset at hand, but incorporate other prior knowledge in an optimal way.
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We exploited the dataset from a published artificial spike experiment [ 11, 14]; the complete dataset was obtained as a personal communication by Semyon Kruglyak [See additional file 1].
However, the methods for exploiting the datasets captured inside the systems are rather lacking.
A specific feature of the present essay, which may make this overview if not comprehensive at least of particular interest, is that the reader is guided through the various traditional and more up-to-date methods by the central thread of techniques developed to measure soil moisture interwoven with applications of modeling tools that exploit the observed datasets.
In summary, we propose that genome-wide multi-locus analysis is performed on available datasets of common diseases, because they can exploit the large genetic datasets and computing resources becoming available, to open a new phase of genetic analysis.
In contrast to classical low-rank and sparse model, we use rank-one and transformed sparse model to exploit the correlations in the dataset.
We therefore exploited the present dataset on tissue types to compare the pairwise correlation of expression of genes from homolog clusters.
Author's response: The aim of this work is not to predict the mRNA 5'-end and translation star site, but to exploit the currently available EST dataset to improve the present knowledge about the most complete ORF which can be assigned to a known mRNA.
The novelty of our approach is to exploit the multidimensional character of the datasets.
In order to fully exploit the potential of these datasets, it is important to understand the systematic effects associated with the instrument and the analysis pipeline.
We exploited the above datasets to compare our method, the Underlying Approach (UA), with other efficient state-of-the-art approaches in the whole-genome phylogeny reconstruction challenge: ACS [ 7], FFP [ 4]d and FFP RY.
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