We discovered that data quality was significantly improved by imposing the constraint of minimal number of replicates, logarithmic transformation and random error analyses.
They found data quality was significantly improved by imposing the constraint of minimal number of replicates, logarithmic transformation and random error analyses.
Variations among gene expression profiles were compared and we concluded that data processing techniques, including setting minimal number of replicates, logarithmic (log) transformation and random error analyses, appeared to be valuable to improve data quality.
In order to simplify the argument, we define a random variable T denoting the minimal number of DDIs computed from the shuffled data set, and T0 is the corresponding value computed from the original data.
Given a set of random variables, the central idea behind PCA is to calculate the minimal number of independent vectors, called principal components, required to reproduce the entire statistical properties prevailing in the original data set.
An algorithm described in [ 43] generates a random reconciliation of G and S, enumerates all such possible reconciliations, and calculates exactly the minimal number of fixed operations needed to rearrange one reconciliation into the other.
While the goal of these studies is to achieve optimization with minimal number of tests, the approach in these studies has several limitations including sensitivity to the design of the automatons driving the random walk and sensitivity to initial conditions.
Clearly for the healthy patients this results in a minimal number of useful segments such that there were fewer than 10, in this case the deficiencies in each individual were supplemented by random extra segments from other patient groups.
with a minimal number of tools.
An optimal solution is desired to have complete tumor coverage with minimal invasiveness, including minimal number of ablations and minimal number of perforation trajectories.
