Applied multivariate statistical analysis
Applied multivariate statistical analysis
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
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Algorithms that construct protein interaction models are sensitive to variation in the experimentally derived data presented to them. Variation is introduced in the biology, the experiment, the measurement and the algorithm. This paper introduces a methodology for the analysis of the sensitivity of a given modeling algorithm to the time and individual variation in a set of time series data. This paper's generated replicates simulate technical replicates conducted under similar conditions. This procedure can be applied to any interaction modeling algorithm and data set. It is shown that the algorithmic variation introduced by a specific stochastic modeling algorithm, the Continuous Bayesian method, is minimal. Furthermore, it is shown for the Continuous Bayesian method that if replicate sets differ by no more than 5% then there is high expectation that resulting models will be highly correlated. If the replicate data differ by more than 20% then there is small expectation of strong correlation. Specific statistical tests for generated model differences under different perturbations are presented.