Confident predictability: identifying reliable gene expression patterns for individualized tumor classification using a local minimax kernel algorithm.

Background: Molecular classification of tumors can be achieved by global gene expression profiling. Most machine learning classification algorithms furnish global error rates for the entire population. A few algorithms provide an estimate of probability of malignancy for each queried patient but the degree of accuracy of these estimates is unknown.

Generation of random numbers on graphics processors: forced indentation in silico of the bacteriophage HK97.

The use of graphics processing units (GPUs) in simulation applications offers a significant speed gain as compared to computations on central processing units (CPUs). Many simulation methods require a large number of independent random variables generated at each step. We present two approaches for implementation of random number generators (RNGs) on a GPU.