Sentence examples for discriminative terms from inspiring English sources

Tsagkias et al. extract the top-100 most discriminative terms from various news sources and observe that these terms have a strong performance in predicting which articles will be highly commented [71].

Keyword search terms could be further expanded or refined, by reiterating either manually or using feature extraction of discriminative terms using machine-learning methods.

When considering the Molecular Function sub-ontology, most discriminative terms relate to DNA repair and kinase activity that are known to be implicated in signalling pathways regulating the cell cycle (Table 4).

The most discriminative terms related to the Cellular Component sub-ontology refers to DNA-repair complexes MutSalpha and MutSbeta, to lysosome and lytic vacuoles, involved in apoptosis and tumour suppression processes [ 15], and to catenin complex, a key regulator of the Wnt signalling pathway whose alterations are associated to carcinogenesis [ 16].

> -wrap-foot> In agreement with a study on Thermatoga maritima in which the authors identified salt bridges and compactness as major determinants of protein stability (Robinson-Rechavi et al., 2006), we observe that the solvation potential and the interaction potentials on residue-level are the most discriminative terms (P = 0.002 for both terms in paired t-test).

We explore a loss function that combines a discriminative term, and a reconstruction term that uses a decoder-encoder map to learn to rectify transformation-perturbed examples, and demonstrate the validity of the resulting embeddings for one-shot learning.

These shrub species are also discriminative in terms of habitat requirements.

QTL analyses were performed on selected traits (date2.5, date1.5, subproc1 and subproc2 expressed as CNL), which resulted after PCA as the most discriminative in terms of phenotypic variation.

Here, we demonstrated that our approach is highly discriminative in terms of differentiating coherent gene sets from random ones and that it provides biologically sensible evaluations in microarray analysis.

When applied to the gene sets from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Kanehisa et al., 2006), the metrics were shown to be highly discriminative in terms of differentiating known coherent gene sets from random ones; when tested on gene sets derived from microarray analysis, the metrics provided biologically sensible assessments.

The proposed ADLRR is formulated from two views: a low-rank matrix recovery term for salient regions detection and a discriminative learning term adopted to distinguish object regions from all super-pixels.