The parallel compression methods balance between feature retention error and compression ratio and perform fast decompression for interactive data visualization.
By gradually increasing the size of feature retention, one can empirically optimize the number of features for classification/prediction. Lastly, a 'global (most important) feature set (S FFS )' is created, which is the union of the retained feature sets from all three settings.
In summary, integrated primary ion chromatographic peaks from all the samples are binned based on chromatographic features of retention index and ion mass, well correlated bins within the retention index window are grouped to create a library entry to represent a chemical entity.
They proposed a decision tree-based prediction of the most frequent substructures, based on mass spectral features and retention index information, for classification of unknown metabolites into different compound classes.
Moreover, in the light of the porosity features, gas retention and viscous flow is revealed to play key roles in the formation of the inner structure of Cf/SiCm.
PHA2 features sodium retention, hyperkalemia, mild hyperchloremic metabolic acidosis, and hypertension.
Non-annotated mass/charge (m/z) features, column retention time, and ion intensity were collected as data.
Data were extracted as non-annotated mass/charge (m/z) features, column retention time, and ion intensity.
When looking at single features, phyletic retention, a measure of the number of organisms an ortholog is present in, was the most predictive of essentiality.
