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Finally, the paper presents a case study for IVRA that illustrates the usefulness and significance of the algorithm.
In order to evaluate the significance of the algorithm used to predict essential genes, it is necessary to vary both the network structure and the algorithm to predict essentiality.
Future validation studies using microarrays containing all 160 genes will help determine if the signature contains redundant information, or if the performance statistics generated herein are an underestimate the true prognostic significance of the algorithm.
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The latter is of special significance for the algorithm's nonlinear stability.
In addition, using unpaired t-Test, we have shown the statistical significance of the OPSO algorithm against several contending algorithms including top-ranked CEC 2015 algorithms.
Results clarify the significance of the proposed algorithm and verify its performance.
To compare the significance of the proposed algorithm, we have done Wilcoxon's rank-sum statistical tests.
As a result of application of this approach we obtain a combination of weight coefficients (alpha ^{j}) that determine the significance of the basic algorithm b(x).
We also demonstrate practical significance of the developed algorithm by comparing bacterial diversity and structure across different skin locations.
In this way, by comparing the significance of the prediction algorithm in different network structures it is possible to find the most significant result among the tested networks.
To objectively assess the significance of the prognostic algorithm developed, an independent validation series of 60 Affymetrix U133 Plus 2.0 profiles from stage 2 and 3 colon cancer patients from another previously published study was used (Jorissen et al, 2009).
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