Supervised machine learning algorithms for protein structure classification
Computational Biology and Chemistry
Multi-class protein fold recognition using large margin logic based divide and conquer learning
Proceedings of the KDD-09 Workshop on Statistical and Relational Learning in Bioinformatics
Computers in Biology and Medicine
Learning Large Margin First Order Decision Lists for Multi-Class Classification
DS '09 Proceedings of the 12th International Conference on Discovery Science
Multi-Class protein fold recognition using large margin logic based divide and conquer learning
ACM SIGKDD Explorations Newsletter
Margin-based ensemble classifier for protein fold recognition
Expert Systems with Applications: An International Journal
Ensemble of diversely trained support vector machines for protein fold recognition
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Protein fold recognition using segmentation-based feature extraction model
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Enhancing protein fold prediction accuracy using evolutionary and structural features
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
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Motivation: The number of protein families has been estimated to be as small as 1000. Recent study shows that the growth in discovery of novel structures that are deposited into PDB and the related rate of increase of SCOP categories are slowing down. This indicates that the protein structure space will be soon covered and thus we may be able to derive most of remaining structures by using the known folding patterns. Present tertiary structure prediction methods behave well when a homologous structure is predicted, but give poorer results when no homologous templates are available. At the same time, some proteins that share twilight-zone sequence identity can form similar folds. Therefore, determination of structural similarity without sequence similarity would be beneficial for prediction of tertiary structures. Results: The proposed PFRES method for automated protein fold classification from low identity ( Availability: The method is freely available from the authors upon request. Contact: lkurgan@ece.ualberta.ca Supplementary information: Supplementary data are available at Bioinformatics online.