Sentence examples for robustly classified from inspiring English sources

This classification rule is based on the observation that for an image with a bimodal histogram, pixels can be robustly classified into two classes.

A total of 999 CDS have been classified and 37 CDS were not robustly classified.

To determine whether Msi overexpression is specific to a particular cancer cell state, we focused on breast cancer, where tumors with distinct properties can be robustly classified by gene expression (Parker et al., 2009; Cancer Genome Atlas Network., 2012).

While the putative Trichoplax Lhx6/8 (Arrowhead) ortholog encodes only two LIM domains but no homeodomain, it can nevertheless be robustly classified as a member of the Arrowhead/Lhx6/8 subfamily.

Our recent study demonstrated that based on functional modules, i.e., GO categories enriched with differentially expressed genes (DEGs), cancer types can be precisely and robustly classified by supervised classification analysis [ 5] or discovered by clustering analysis [ 7].

Comparing classifiers, one can see the higher ability of CA to robustly classify composers by analyzing Table 10: while CA's higher standard deviation may be regarded as a drawback, its lowest recognition rates rival the best performances of SVM and 1NN consistently for all experiments, hence justifying its use as a classifier.

However, it is not clear what RMSD cutoff one should use in the presence of noise to robustly classify a structure as variable.

To validate that our common caner signature can correctly and robustly classify tumors, we applied our panel to data from several published studies on tumors originating from different tissue types.

As a consequence, one can robustly classify genes whose total and polysomal-associated mRNA levels are coupled (high PCC and Z-score of ≥1·96) as well as genes that display an uncoupling of both mRNA levels (low PCC and Z-score of ≤ 1·96).

While chemists have been very successful in developing a standardized nomenclature (IUPAC) and standardized methods for drawing or exchanging chemical structures [11, 12], the field of chemistry still lacks a standardized, comprehensive, and clearly defined chemical taxonomy or chemical ontology to robustly characterize, classify and annotate chemical structures.

We show that the energetic patterns obtained from these simulations can be used to robustly rank and classify known cleaved and uncleaved peptides and that these structural-energetic patterns have greater discriminatory power compared to purely sequence-based statistical inference.