Using this small-scale data set allowed missing data to be reduced (from 78% in the large scale dataset to 65% in the smaller dataset) and the use of Bayesian inference under the often better-fitting CAT-based models in PhyloBayes v.3.3 [ 33].
Large-scale datasets to enable further research on all of the subtasks proposed as well as on other aspects of video understanding.
Scientists have compared the recent availability of these large-scale datasets to the invention of the microscope [1] and new fields such as Computational Social Science [2] are emerging.
While we have the starting points for the analysis, we are looking for students with solid background in stochastic processes or statistics, and the desire to work with large-scale datasets, to turn our preliminary hunches into definitive data-driven insights and conclusions.
Curators use a variety of published, large-scale datasets to identify novel, inaccurate and alternative gene structures.
One of the inherent challenges in transcriptome profiling is the effective analysis of large-scale datasets to optimize extraction of relevant biological meaning.
It confirms the approach of mining large scale datasets as a robust method to define reference genes, but cautions against using gene orthology or counterparts of reference genes in other plant species as a means of defining reference genes.
Scientific publication is an efficient means of knowledge dissemination but there should be encouragement of authors to place large scale datasets into the public domain.
However, GP regression is restrictive for large scale datasets due to its computation complexity.
With the growing data science trend, we always need a large-scale dataset to efficiently solve a problem.
To better understand the scalability of GEDT to large scale datasets, the results of the timing experiments for the larger SNP datasets are shown in Table 4.
