The benchmark sensitivities are obtained by finite difference, which involves solving the nonlinear eigenproblem at least as many times as the number of parameters.
Coupled with development of the reactor physics benchmark evaluation, sensitivity analysis has been performed.
The proposed method is compared against a gradient-based method, seven meta-heuristics, and the trial-and-error method on two control benchmarks using sensitivity analysis and full factorial parameter selection and the results are validated using one-tailed T-test.
To benchmark the sensitivity (true positive rate) and selectivity (false positive rate) performance of each GH signature, profile hidden Markov models (profile HMMs) were derived using HMMER3 software [43].
However, CPORT shows exactly the reverse: among the proteins in the benchmark, the sensitivity of the prediction is negatively correlated with the number of predicted interface residues (r = −0.35).
In this study we have used the high-quality annotations of these fungi to benchmark the sensitivity and specificity of CodingQuarry, and compare it to other gene predictors.
We used the available HuRef genome to produce short paired-end reads, similar to reads generated by the Illumina technology (simulating an Illumina sequencing of the Venter genome) to benchmark the sensitivity and specificity of our algorithms.
First, as NA12878, which is included in our study, is a well-studied individual genome, we benchmark the sensitivity of AsmVar by comparing the NA12878 AsmVar non-reference genotype calls to the 21415 dbVar structural variation records for this individual [ 5].
This goal includes validating the platform against the Affymetrix microarray platform, benchmarking and optimizing sensitivity and precision, and using the platform to measure insulin-like gene expression during the C. elegans life cycle.
Furthermore, we have benchmarked the inherent sensitivity and robustness of deriving associations from such responses.
