Utilizing these modifications our code outperforms both MKL1 CubyAS2 by up to 6 times on the self-hosted Intel KNL (codenamed Knights Landing) and Kepler GPU architectures, for large numbers of double precision GEMM operations using matrices of size 2 × 2 to 20 × 20.
We emphasize that, with appropriate modifications, our method of bootstrap resampling accompanied by significance clustering is general and works for any type of network and any clustering algorithm (see Materials and Methods for a detailed description of the method).
Including these gene modifications, our model predicted an increment of ethanol production of 5%, no glycerol production and consumption of acetate instead of production.
Moreover, since we did not survey all possible H2B modifications, our understanding of H2BE's modification status is by no means complete.
With some modifications, our method has the potential to be extended to gene segments in the α, γ, and δ TCR loci, as well as the genes encoding for B-cell receptor chains.
Using all 24 histone modifications, our approach can accurately predict promoters with ∼92% true-positive (TP) rate and ∼1.6% false-positive (FP) rate in H, whereas in IMR90 we observed even better performance (TP ∼95%, FP ∼0.3%).
