These results were consistent with our initial intuition that incorporating TF-TF interactions may improve the predictive model, and hence we recommend the final trained model for predictive purposes (to classify a new sequence as being bound to the TF or not).
A classification method has been purposed to classify microbial genomes.
The purpose was to classify the errors determined by the global algorithm, depending on the atmospheric situation at each moment.
A second purpose was to classify barriers using an ecological framework and to examine the pattern of barrier categories (i.e., intrapersonal, interpersonal, institutional, community, public policy, and physical environmental) and specific barrier types as grade increased.
Its purpose was to classify the encounter into one of two states; the patient had influenza like illness or not.
Our purpose is to classify the lip images into four groups: deep-red, red, purple and pale.
However, if there is no censoring, and the purpose is to classify patients' survival risks at a specific time point of interest, then a binary classifier should be more powerful than a survival prediction model.
This is a useful tool used for classification and regression purposes, e.g., to classify structures in the Interstellar Medium (Beaumont et al., 2011), or AGNs from stars and normal galaxies (Zhang et al., 2002), or to select QSO candidates from the MACHO LMC database (Kim et al., 2011a, b).
Granularity could also be improved by encoding high priority discrete variables for not just billing purposes, but to classify patients for phenotypic properties.
For diagnostic purposes, the aim is to classify the individual trajectories into a limited number of groups; thus, an alternative to mixture models is necessary.
