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
Learning Large Margin First Order Decision Lists for Multi-Class Classification
DS '09 Proceedings of the 12th International Conference on Discovery Science
Using DNA to generate 3D organic art forms
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
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
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Computers in Biology and Medicine
Mining of protein contact maps for protein fold prediction
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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: Fold recognition is a key step in the protein structure discovery process, especially when traditional sequence comparison methods fail to yield convincing structural homologies. Although many methods have been developed for protein fold recognition, their accuracies remain low. This can be attributed to insufficient exploitation of fold discriminatory features. Results: We have developed a new method for protein fold recognition using structural information of amino acid residues and amino acid residue pairs. Since protein fold recognition can be treated as a protein fold classification problem, we have developed a Support Vector Machine (SVM) based classifier approach that uses secondary structural state and solvent accessibility state frequencies of amino acids and amino acid pairs as feature vectors. Among the individual properties examined secondary structural state frequencies of amino acids gave an overall accuracy of 65.2% for fold discrimination, which is better than the accuracy by any method reported so far in the literature. Combination of secondary structural state frequencies with solvent accessibility state frequencies of amino acids and amino acid pairs further improved the fold discrimination accuracy to more than 70%, which is ~8% higher than the best available method. In this study we have also tested, for the first time, an all-together multi-class method known as Crammer and Singer method for protein fold classification. Our studies reveal that the three multi-class classification methods, namely one versus all, one versus one and Crammer and Singer method, yield similar predictions. Availability: Dataset and stand-alone program are available upon request. Contact: han@cdfd.org.in Supplementary information: Supplementary data are available at Bioinformatics online.