Sentence examples for classifications of the effect from inspiring English sources

Exact(1)

Classifications of the effect of X1 based on the fact model are then used as the gold standard to compare with the classifications based on the conventional approach and the alternative approach.

Similar(59)

Additionally, it provides various statistics on the set of paths linking two signaling nodes as well as a classification of the effect of each path as either coherent or incoherent (e.g. [ 23]).

Among higher levels of BMI classification, the effect of increasing age became stronger.

Table 5 shows the classification of the decomposition effects according to the definition of the ratio between technology (TCE) and production effect (PE).

According to the Evans classification of the therapeutic effects of preoperative CRT, the patients in this report demonstrated pathological antitumor effects of grades IIa and IIb, respectively.

A review of six popular introductory genetics textbooks reveals that gene duplication is either not mentioned, or is restricted to a page or two toward the end of the books, while Muller's classification of the phenotypic effects of mutations, does not occur at all.

We extracted data on the health effects of PCB or PBDE exposure for each experiment of each selected article when available and assigned them according to a slightly modified classification of the health effects used in ATSDR's (US Agency for Toxic Substances and Disease Registry) Toxicological Profiles.

A systematic classification of the side effects of tongue piercing documented in the literature suggests that three types of complications can be distinguished: complications during piercing (complications during the initial procedure), complications immediately following piercing (primary postoperative complications) and long-term complications (secondary postoperative complications) [ 5].

In our analysis, we use both classifications when assessing the effect of chaperonin buffering.

The filter is used as a preprocessing step, prior to classification, to get rid of the effect of resources adjustment on the collected data, in order to normalise the data with respect to the original infrastructure on which the training was performed, before passing it to the SVM classifier.

This two-state classification allows precise evaluation of the effect of base flipping on local backbone degrees of freedom.

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