The performance results obtained from the feature extraction phase indicates that the proposed approach performed comparably with related works in terms of recall, precision, and F-Measure.
Using a novel approach, mirCoS performs comparably to or better than existing miRNA prediction methods, and contributes a significant number of new candidate miRNAs for experimental verification.
Though our models were developed using PAH concentrations in visceral tissue, the approach may perform comparably for tissues more commonly consumed by the public.
Compared with other state-of-the-art evolutionary algorithms (EAs), our approach performs better, or at least comparably, in terms of the quality and stability of the final solutions.
We compare the performance of our approach to a recently proposed method based on quantile regression, and demonstrate that our method performs comparably to this approach in terms of two widely used methods for assessing the quality of probabilistic scenarios: the Energy score and the Variogram score.
Comparing our approach to other PPI predictors, we find that ENTS performs comparably to or better than a number of existing approaches, including several that utilize a variety of functional information for their predictions.
However, multiple studies have shown that it performs comparably to other single-dataset approaches [ 33- 35].
We observed that probe level filtering based only on perfect match probe intensities performs comparably to the perfect match and mismatch based approach of Ji et al. One possible reason that these two filtering procedures perform similarly despite fairly different implementations is because the Ji et al. procedure uses the Affymetrix MAS5 algorithm to obtain summary expression intensities.
We illustrate that LD-Spline performs comparably to the four-gamete rule and the Gabriel et al. approach; however as a SNP-centric approach LD-Spline has the added benefit of systematically identifying a genomic boundary for each SNP, where the global block partitioning approaches may falter due to sampling variation in LD statistics.
Results: The hierarchical inference outperforms related (but non-hierarchical) approaches when the networks used to generate the data were identical, and performs comparably even when the networks used to generate data were independent.
