The developed optimization model was applied in two benchmarks available in the literature.
In two benchmarks (VariBench/SwissVar), the performance of our unweighted method is comparable to another sequence-based method: SIFT [Ng and Henikoff, 2001], and to a sequence/structure-based method: PolyPhen-1 [Ramensky et al., 2002].
The Indonesian central bank's increase in three benchmark rates came on the same day that central banks in New Zealand, the Philippines and South Korea decided that their economies were robust enough to withstand the expected start by the Fed in reduction of its bond-buying program.
The method was assessed on its ability to reproduce the native contacts in five benchmark cases with simulated MS data and two cases with real MS data.
Our tunable hybrid allocator design shows 11 54% better performance and nearly equal memory consumption when compared to the well known Doug Lea allocator in seven benchmark programs.
Results of applying the proposed algorithm in three benchmark pipe networks show that SLC converges to the global optimum more reliably and rapidly in comparison with other meta-heuristic methods.
Next, we proposed a method for prediction of MHC ligands combining MHC class II binding predictions with local structure prediction, and demonstrated that this method consistently in three benchmark studies significantly improved the prediction accuracy of a method based on MHC binding alone.
