Your English writing platform
Discover LudwigSimilar(60)
Our recommendation is that, in practical analysis, researchers need to experiment with different sets of representative features.
With the 6 real datasets analyzed, 3 different sets of representative features have the best prediction performances.
The four sets of representative features investigated in this study share some desired properties with other principal components-based analysis.
(2) By incorporating the kernel technique into the factor analysis, a new feature selection method (KFA) is presented to exploit the nonlinear representative features with non-Gaussian distributions.
With (R1 - R4), respectively, we randomly select 10 representative features as associated with prognosis and set the rest as noises.
Chemical descriptors are the representative features of chemical molecules that are responsible for its activity.
Thus, there are a total of 4 different data-generating models, with (R1 - R4) being the "true" representative features.
However, correlations between the three molecules with clinical features of lung cancer are unexplored.
In addition, it is possible that only a subset of the representative features is associated with cancer survival.
With the training set, we use the representative features (R1 - R4) and proposed regularized approaches for estimation.
Examining the concordance indices suggests reasonable predictive power of the representative features and similar conclusions as with the logrank statistics.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com