Sentence examples for predictions to extract from inspiring English sources

Computational modeling can compile and classify data sets in a quantitative manner and consequently provide testable predictions to extract vital information not readily apparent by conventional analytical techniques.

We used PPI bioinformatic predictions to extract all available human proteins that are related to MAWD and MAWBP, and we described their global properties.

It allows the user to display a graphical repository line for comparison among different predictions, to highlight zones of interest, to retrieve sequence boundaries for each prediction and to extract parts of sequences corresponding to a prediction or a highlighted area.

We performed a class prediction analysis to extract the most meaningful genes indicative of disease activity and inflammation.

This failure motivated us to develop a novel prediction method able to extract functional cis-acting elements without clustering.

Step 3: In order to improve prediction accuracy, and to extract small subsets of independent variables seemingly with the strongest effects on the dependent variable, we used the least absolute shrinkage and selection operator (LASSO) [ 39] to identify which of the ICF categories are independently associated with the log-transformed workload.

To extract predictions from such a prediction model and multiply imputed data, either the sets of imputation-specific regression coefficients or the pooled regression coefficients could be used, and we must decide how to handle individuals with multiply imputed covariates.

While the PFP gives a general statistical measure of the reliability of our predictions, it is possible to extract a high-confidence subset of predicted annotations by estimating the precision associated to each individual GO term through a leave-one-out procedure.

The methods considered here use a variety of techniques to extract predictions from the PPI network, and consequently each might be expected to make successful predictions for genes not correctly handled by other methods.

In conclusion we have shown that Genetic Programming outperforms other machine learning methods as a tool to extract predictions from an established breast cancer gene signature.

SigTerms [ 23] was utilized to extract predictions from PicTar [ 24], TargetScan (4.1 and 5.1) [ 25] and miRanda (Jan 08 and Sep 08) [ 26]. miRNA target predictions were extracted two separate times, the first using TargetScan release 4.1 and miRanda release Jan 08 and the second using TargetScan release 5.1 and miRanda release Sep 08.