Sentence examples for classification beyond from inspiring English sources

Cardiorespiratory fitness (CRF) has shown to improve the classification beyond traditional risk factors and cumulative lifetime risk of death, however, there is no formal multicenter database that provides representative sample on a global scale to accurately interpret CRF measures.

To assess each classification beyond a theoretical model, we created a set of 12 different clinical case-scenarios (Figure S4).

A distinct subgroup of BRCAx tumors defined by methylation profiles was identified, supporting the hypothesis that DNA methylation profiling might expand subtype classification beyond mutation analysis.

Also, when added to a model based on traditional risk factors, IL-6 and activin A, but not E/Em or post-exercise recovery loop, improved risk classification beyond conventional CV risk factors as assessed by C-statistics and NRI.

The PARADIGM clustering seems to expand classification beyond traditional immunohistochemistry, as a distinction was found between two clusters within luminal A breast cancer (called the PDGM3 and PDGM4 clusters) and luminal B breast cancer (PDGM4) clusters.

In the realm of medicine, inclusion of clinical and functional genomic data may enhance risk classification beyond what can be done with genomic data alone (Patel et al. 2013), particularly where the relationship between the static genome and dynamic gene function changes over the lifetime of an organism.

There are also many other remarkable results, especially for actions of compact groups (see, e.g., [51], Chap. IV], [62]), but, to the best of our knowledge, no complete explicit classifications beyond group dimension ((n-1)(n-2)/2+2), with (nge 6), have been found.

Alongside Manhattan socialites, Beverly Hills hipsters and rich white women from Greenwich and Bedford, Conn., Short Hills ladies merit their own classification in "Beyond the Blonde" (Warner Books), by Kathleen Flynn-Hui, a Manhattan hair colorist.

For women, clear weight-based patterns in the data are apparent as BMI classification increases beyond normal weight.

A complete description of SVM classification is beyond the scope of this article and only a brief summary is presented here; the interested reader is referred to [52, 53] and the references therein for more detail.

Although there are many toolboxes that can be exploited, such as [34, 35], the study of the impact of inaccurate skeleton/pose estimation on activity classification is beyond the scope of this paper.