Hybrid ensemble
Overview
Our hybrid ensemble combines the template-based tool Nnessy with the template-free tool Porter. This hybrid approach takes the output prediction from Nnessy and compares the estimated accuracy of the prediction to a threshold. If this estimated accuracy exceeds the threshold, Nnessy's prediction is returned. If not, Porter's prediction is returned.Results
On average over standard CASP and PDB benchmarks, the hybrid exceeds the state-of-the-art 3-state accuracy by nearly 4%, and exceeds the 8-state prediction by more than 8%.Publication
The methods implemented in Ssylla are given in the following publication, which should be cited under noteworthy use of Ssylla- Spencer Krieger and John Kececioglu, “Predicting protein secondary structure by an ensemble through feature-based accuracy estimation.” Proceedings of the 11th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), 29, 1-10, 2020.
Video
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The following video was presented at ACM-BCB 2020 and gives more detailed information on the hybrid ensemble:
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