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All the participated methods are reported in [11] for reference.
The prediction results from all participated methods have been released online (http://predictioncenter.org/download_area/CASP9/server_predictions/), allowing us to make comparisons based on individual predictions.
Similar(58)
As shown, the proposed method showed the best performance among 7 participating methods.
Table 5 shows a comparison of the four runs of our approach on the Princeton Shape Benchmark; these runs outperform the other participating methods (described in [4, 10, 34]).
This perhaps explains the decline in performance (relative to other participating methods in DREAM3) for the smaller networks in this challenge.
The performance of the two top-ranked algorithms is almost identical, and is much better than that of the other participating methods.
Clearly all the participating methods achieved better scores for an in silico network than for either one of in vivo networks.
We proposed a sequence-based machine learning method, EASE-MM, which predicts stability changes as a consensus of predicted probabilities of two participating methods, EASE-ASA and EASE-SS.
WCY participated in method development, implemented the ScanBMA method, carried out empirical studies comparing ScanBMA to other competing methods, and drafted the manuscript.
GM was the principal investigator in the design of the study and methods, participated in the statistical methods, carried out the statistical analysis and drafted the manuscript.
MBZ participated in method development and revision of the manuscript.
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