Sentence examples for data for discovering from inspiring English sources
Lee and colleagues (2003) applied the SSVS method to the problem of gene selection with microarray data for discovering significant disease genes on breast tumors.
2, 12– 15 For example, Harpaz et al. applied Association Rule Mining (ARM) to the FDA's AERS data for discovering detrimental associations of multiple drugs to multiple ADEs.
However, mining microarray data for discovering complicated regulatory relationships is still challenging partially due to the fact that these data are often incomplete, noisy, and contain misleading outliers, all of which likely produce false positives in biological inferences.
As one of the future research directions, more efforts are still need to be made to evaluate the performance our method on different data, especially include quantitative information such as microarray and proteomics data for discovering disease mechanism in the gene expression level or protein level.
The term of association rules is a powerful technique of data mining for discovering correlation and relationships between objects in the database.
Our results indicate that GeneRIFs are an excellent data source for discovering disease-gene relationships.
Genome-wide time-series data provide a rich set of information for discovering gene regulatory relationships.
Clustering (Hartigan 1975; Jain and Dubes 1988; Oliveira and Pedrycz 2007) is a widely used technique in data mining application for discovering patterns in underlying data, which partitions the input space into K regions depending on some similarity/dissimilarity metric, where the value of K may or may not be known a priori.
Recently, rather than electronically storing clinical materials or processing each data, the needs for discovering meanings from medical big data and utilizing them have significantly increased.
Finally, clustering biological data would be useful not only for exploring the data but also for discovering implicit links between the objects.
The association rule is a powerful data mining technique for discovering a correlation between variables in the database.
