Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction

Scientific Reports 2019 Mirko TorrisiManaz KaleelGianluca Pollastri

Protein Secondary Structure prediction has been a central topic of research in Bioinformatics for decades. In spite of this, even the most sophisticated ab initio SS predictors are not able to reach the theoretical limit of three-state prediction accuracy (88–90%), while only a few predict more than the 3 traditional Helix, Strand and Coil classes... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Protein Secondary Structure Prediction 2017_test set Porter5 Q3 84.19 # 1
Protein Secondary Structure Prediction 2017_test set Porter5 Q8 73.02 # 1
Protein Secondary Structure Prediction 2019_test set Porter5 Q3 81.74 # 1
Protein Secondary Structure Prediction CB513 Porter5 Q8 0.74 # 1
Protein Secondary Structure Prediction Jpred4 blind set Porter5 Accuracy 84.62 # 1