Sentence examples for genomic datasets such from inspiring English sources

This concept can be extended to handle very large genomic datasets, such as NCBI GenBank or a proprietary database.

Second, and more importantly, as the complete conditionals are available in closed forms, the Bayesian formation of the lasso substantially aids our Bayesian computations for large genomic datasets such as those considered here.

We therefore used a combination of in-silico analysis of the p63 binding sites and careful data mining of large-scale genomic datasets such as RNA-Seq and ChIP-Seq from the ENCODE project.

These results suggest that Logit-Lapnet is more accurate than either Lasso or elastic net for identifying biomarkers from large multidimensional genomic datasets such as those generated by the TCGA.

Based on these assumptions, a number of studies focus on developing computational frameworks for discovering disease-related gene candidates by exploiting complex associations between phenotypes and genotypes found within heterogeneous genomic datasets such as gene expression data, protein-protein interaction networks [ 5, 6] and gene ontology annotations [ 7].

Genomic features currently must be manipulated with reference to the underlying genomic sequence, which can make working with post-genomic datasets, such as microarray results, overly complex.

As these examples illustrate, we welcome the submission of significant technological or methodological advances, including genomic or other datasets (such as brain atlases), collections of biological resources, software tools, and so on, especially when linked to examples that demonstrate their broad utility.

This is largely due to the need to handle genomic datasets of unprecedented sizes, such as genome-wide dense markers or sequences for genome-enabled selection programs [ 2].

The collection of large, near-comprehensive genomic datasets of human pathogens such as Candida albicans has become common due to the availability of next-generation sequencing technologies.

GenomeGraphs (Durinck et al., 2009) is a R package, which allows the visualization of one genomic region with related datasets such as microarray data.

Given the size of the genomic datasets, dimensionality reduction methods such as principal components analysis, information gain and multifactor dimensionality reduction will be essential to make complexity algorithms tractable (Hahn et al., 2003; Statnikov et al., 2005; Yeung and Ruzzo, 2001).