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This chapter discusses the novel database schema to help neuroscientists in managing complex datasets and implementation that allow datasets from diverse labs to be stored coherently in a single framework.
Today a wider range of soil properties can be considered for model development (new training datasets) and implementation, as in new digital soil property maps (Adhikari et al., 2013; Arrouays et al., 2014).
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We outline the dataset and implementation below.
We describe here the behaviour of three variables (number of uniform datasets, implementation cost and implementation time) for a hypothetical period of five years.
We first introduce the MGC dataset and the implementation details of experiments.
To pursuit this aim, we applied a well-established tool of multivariate analysis (i.e., the partial correlation) to each selected mRNA/lncRNA pair with respect to each miRNA in our dataset (see Algorithm and implementation subsection).
Availability and implementation: Code, datasets and networks presented in this article are available at http://bonneaulab.bio.nyu.edu/software.html.html
Availability and implementation: All datasets, software and a searchable Web site are available at http://mirpd.jensenlab.org.jensenlab.org
Availability and implementation: Simulated datasets and other supporting information can be found at http://bioinf.itmat.upenn.edu/BEERS/bp2 Supplementary information: Supplementary data are available at Bioinformatics online.
Future work includes automatically learning the rules, providing support for online analysis of materialized datasets and improving the prototype implementation. 1 "Data integration is the problem of providing unified and transparent access to a set of autonomous and heterogeneous sources, in order to allow for expressing queries that could not be supported by the individual data sources alone".
Availability and implementation: All the datasets and the code used in this study are freely available at https://collaborators.oicr.on.ca/vferretti/borozan_csss/csss.html.html
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