Sentence examples for a bin based on from inspiring English sources

Instead of purely using statistics to define the representative of each bin, we chose the representative HAPs for a bin based on 1) previously reported associations as well as 2) the level of correlation of each HAP with the other HAPs in the bin.

SNMs within genes were assigned a bin based on the transcript in which they reside; SNMs not mapping to a transcript were assigned a gene expression of 0. Relative mutation rate was then determined in an analogous manner as G/C content and replication time.

Functional predictions and Bin Ids were generated by searching a variety of reference databases (currently 6 are available: 3 BLAST-based, 2 reverse position-specific BLAST based and InterProScan) and subsequently evaluating and compiling the search results for each input gene to propose a functional Bin based on the manually curated binning of the reference database entries.

Because SVSS is most appropriate when variables are correlated (i.e., ρ = 0.25-0.80) but not highly correlated (i.e., ρ > 0.80), we grouped (or "binned") HAPs with high correlation (i.e., ρ > 0.80) and selected pollutants to represent a given bin based on existing science and correlations with the other HAPs within the bin.

Each pixel casts a weighted vote for an edge orientation histogram bin, based on the orientation of the image gradient at that pixel.

In this study, we instead use an "approximate length-conditional" approach, which assumes that each fish is a random sample from that length bin based on an equilibrium population age structure, to fit age-length data from three previous studies.

Samples were analysed using the GeneMapper v3.7 software, which assigned each TDF an allelic label, or bin, based on its size as determined by comparison to the ILS600-C marker (Promega).

Poisson noise was then added to each sinogram bin based on the mean counts in the bin to generate 25 different noise realizations of sinograms.

Figure  5 shows NRS and NRD metrics for each bin, based on GC% of targets.

All the data acquired between this point and the next full inspiration is divided evenly between a predetermined number of bins based on a percentage of the respiratory cycle.

Following this, noise estimates from multiple signals are partitioned into bins based on a variable that correlates with the noise amplitude, such as measurement channel or signal intensity.