Sentence examples for probability of driver from inspiring English sources

There is no doubt that cell phone use while driving can lead to a higher probability of driver error, which increases the likelihood of more crashes.

In addition to modeling key features of the observed mutation data, MADGiC also leverages the non-random mutational patterns observed across many cancer types in the COSMIC database to inform the prior probability of driver activity.

The Bayesian framework provides a natural way to leverage the mutational patterns observed in COSMIC as prior information and provides gene-specific posterior probabilities of driver gene activity.

Based on the threshold and the algorithm score, we calculated the probability of the driver being cautious or reckless.

MADGiC identified 19 genes with a posterior probability of being a driver greater than 0.95 (listed in Supplementary Section S5).

Thus, under the global posterior, the genes from a set of lesions are assigned the same probability of being a driver, independent of the size of the lesions they belong to.

This is also a unique opportunity to assess the utility of a road safety campaign to see if it helped to reduce the probability of becoming risky driver following deployment.

In the latter step, the statistical confirming procedure, the two lists of possible OGs and TSGs undergo four tests to assess whether the mutational pattern in each gene shows statistically defined evidence of positive selection based on the mutation rate and the number of non-silent mutations, calculating their statistical probability of being true driver mutations.

In this section we present the road network, the generated load, our performance metrics, the different probability distributions for generation of driver preferences, and the other traffic management methods that are compared to our approach in Section 'Results and discussion.'.

The presented analysis suggests that structurally conserved hotspots in the kinase catalytic domain may be statistically enriched by mutations with a high probability of being cancer drivers.

The model was then used to evaluate the effect of policy variables on the yielding probabilities of the drivers.