Sentence examples for data in discovering from inspiring English sources

These results underscore the value of metagenomic data in discovering signature genes that play important roles in the environment through their expression, as demonstrated by integrases in lysogeny.

Recently, however, the development of the mammalian and human phenotype ontologies (Robinson et al., 2008; Smith and Eppig, 2009) – which describe phenotypes associated with mouse mutants or human diseases – has allowed us to quantitatively estimate the usefulness of different types of mouse phenotype data in discovering candidates for human diseases.

In this paper Formal Concept Analysis (FCA) is showcased for its exploratory data analysis capabilities in discovering domestic violence intelligence from a dataset of unstructured police reports filed with the Amsterdam-Amstelland police in the Netherlands.

Large-scale data analytics can aid in discovering patterns in these data to gain new scientific insights; however, the domain of biology is very different from most other domains in this aspect.

The bill refers to "bulk data" as "a vital tool in discovering new targets and identifying emerging threats".

The ability to incorporate measurement data into analysis is vital in discovering condition-specific pathways.

These data have been hugely informative in discovering driver mutations that are causally responsible for the development and progression of cancer (Garraway and Lander, 2013, Vogelstein et al., 2013, Wheeler and Wang, 2013).

The soft computing based systems are capable of extracting relevant information from large sets of data by discovering hidden patterns in the data.

Generally, it includes descriptive (describes data), exploratory (discovering unknown correlations in data), predictive (predict events and trends) and prescriptive (suggest actions) methods to gain meaningful insight for different domains [56, 57].

With the advent of high-throughput methods and sheer volume of medical publications covering various diseases, biomedical researchers face challenges of distilling an enormous amount of data and discovering knowledge buried in them.

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.