Selected article for: "null distribution and test statistic null distribution"
Author: Brandon Malone; Boris Simovski; Clement Moline; Jun Cheng; Marius Gheorghe; Hugues Fontenelle; Ioannis Vardaxis; Simen Tennoe; Jenny-Ann Malmberg; Richard Stratford; Trevor Clancy
Title: Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs
  • Document date: 2020_4_21
    • ID: cm30gyd8_62
    Snippet: As with genomic tracks [38] , analytical approaches to estimate the statistical significance of multiple observed HLA tracks are computationally intractable. An effective alternative to this problem is Monte Carlo-based simulations. A null model is defined, as the generative model of the HLA tracks, if they were generated by chance. From the null model, through sampling, arises the null distribution of the test statistic Si. The null model must r.....
    Document: As with genomic tracks [38] , analytical approaches to estimate the statistical significance of multiple observed HLA tracks are computationally intractable. An effective alternative to this problem is Monte Carlo-based simulations. A null model is defined, as the generative model of the HLA tracks, if they were generated by chance. From the null model, through sampling, arises the null distribution of the test statistic Si. The null model must reflect the complexities behind the nature of the HLA tracks. Amino acids in one HLA track will always form consecutive groups of length at least 8 (smallest peptide size used in the prediction framework). Similarly, amino acids with low epitope scores will also cluster together.

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