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Analysis of binary encoded genome structures by parsimony-splits algorithm has lower sensitivity and PPV than the rest of methods.
RF had an equal or better predictive accuracy in the pure lines at α = 0.05, 0.25 and 0.50, than the rest of methods used in this study.
The rest of methods find a less percentage of biclusters with the p-values specified than the proposed method although OPSM presents good results for high levels of significance (p >0.05).
In order to further compare our procedure and the rest of methods considered, we calculated differences between the IOL power predicted by our procedure and the different regression formulas considered in the study.
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From this figure we can conclude that normalization coefficients determined with TMM and UQ methods group together and divergent from the rest of normalization methods.
From the results we can see that, when the scale changes are small, our method and Bicubic spline interpolation out-perform the rest of the methods.
For down-regulated genes, 2,521 were identified by the rest of five methods, and for up-regulated genes, 4,067 were identified by all five methods.
The rest of the methods follow similar trends with lower BER degradation corresponding to the lowest power increase of the POCS-based methods.
The recognition rates of the rest of the methods range from 20.84 to 27.54%.
The results showed a better performance in the team with the role-based strategy outperformed the rest of the methods.
The rest of the methods are non-parametric and, thus, not affected by the possible non-normality.
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