Wilcox (1955) proposed a classification based on sodium percentage which can be calculated from the following formula: %Na = (Na+ + K+) × 100/(Ca2+ + Mg2+ + Na+ + K+) (All ionic concentrations expressed in meq/l).
We propose a classification based on three characteristics: (1) The uncertainty across all haplotypes (1a, "overall error measure"), versus the error in a specific haplotype (1b, "haplotype-specific error measure").
He proposed a classification based on clusters obtained with Bayesian inference using a support threshold for posterior probabilities of 0.95.
In 1976 Wolfe proposed a classification based on four different parenchymal patterns [ 18] which was modified into five categories by László Tabár in 1997 [ 19, 20].
Hanano et al. proposed a classification based on evolutionary relationship in which the caleosin genes were divided into three distinct classes: class I, class II, and class III, which coincided with sub IV, sub III, and sub V, respectively [ 17].
Thus, we propose a classification based not only on particle size but on a set of learned visual features that can better differentiate between primary particles (single or grouped) and real agglomerates.
Another substantial work proposes a VUS classification based on a series of 932 early onset colorectal cancer patients registered for clinical data, MMR function in tumor cells, in silico analysis and genotyping studies in both relevant families and control population (17).
Considering the characters of a specific emergency domain, we propose a text classification based on emergency domain words and machine learning technique taking a System Engineering view.
We propose a new classification based on monophyletic groups recovered by phylogenetic analyses.
To study the incidence of and risk factors for postoperative capsular block syndrome (CBS) and propose a new classification based on etiology.
