The information redundancy, noise, and high-data dimensionalities in mass spectral data not only make some traditional classification methods (e.g., Fisher discriminant analysis) lose discriminative power, but also present an urgent challenge in computational proteomics.
We compare the actual predictions made by each method and find that most methods make some correct predictions not made by any other method, and that there are very few incorrect predictions made by multiple methods.
Section 5 provides some numerical examples to illustrate the proposed method and make some comparisons with the existing methods.
The breadth of genomic coverage, GC bias, chimeric DNA molecules, ADOs, preferential allelic amplifications and nucleotide copy errors can vary significantly between different WGA approaches, making some WGA methods better suited than others for specific genetic tests [ 27, 28, 35].
We make some changes of the method.
25 The authors attempt to make some recommendations; however, this method presents a risk of overinterpretation of data.
Make some popcorn with any method you prefer.
But, the method does make some assumptions about the genetic elements that can be uncovered.
The new method does make some assumptions, which, although unlikely to hold exactly, are reasonable for this sort of calculation.
The above simulation scheme describes 2 × 50 × 50 × 6 × 2 = 60 × 10 distinct experimental conditions; this makes evaluating some methods in some populations impractical specifically, DF.MCMC and DF.MCMC.pseudo are not evaluated in simulations involving the HS.
