Sentence examples for region identification from inspiring English sources

Homogeneous region identification is the significant step in regional frequency analysis.

Protein-coding region identification and gene prediction were conducted using a combination of homology-based prediction, de novo prediction, and transcriptome-based prediction methods.

The use of different data sources, as well as of different region identification algorithms, causes discrepancies in reported sunspot areas.

Dynamic crowd monitoring was achieved by implementing the context based region identification and grouping of participants, distributed crowd behavior estimation, and stampede prediction based on distributed consensus.

With the improved method, road region identification and road boundary recognition obtain higher accuracy.

Multi-atlas segmentation has proven useful for automated region identification especially in clinical neuro-applications [24 27].

We followed SPLASH conserved-region identification with motif discovery by DME to identify conserved motifs in these regions, thus fixing motif column values whether they have high or low information content.

The regions that generated reliable accuracies across participants in this single-region identification were bilateral SES, IES, calcarine sulcus, fusiform gyrus, IPS, left IPL, posterior superior, middle and inferior temporal gyri, postcentral gyrus, and hippocampus.

The regions that generated reliable accuracies across participants in this single-region identification analysis were bilateral SES, calcarine, IES, SPL, IPL, IPS, fusiform, posterior superior and middle temporal, posterior inferior temporal gyri, cerebellum, and left precentral, superior frontal, inferior frontal triangularis, insula, and postcentral gyri (Table 1).

The design of sequencing for 'dark-region' identification (i.e. DNA inserts on the sampled genome that are not in the reference) is not addressed.

Processing of ChIP-chip data was performed in three steps: normalization, IP/mock-IP ratio computation (in log2 scale) and enriched-region identification.