An ensemble of support vector machines for predicting virulent proteins
Expert Systems with Applications: An International Journal
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
Combining feature spaces for classification
Pattern Recognition
Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition 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 fuzzy support vector machine network to predict low homology protein structural classes
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Application of classifier fusion for protein fold recognition
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Multi-Class protein fold recognition using large margin logic based divide and conquer learning
ACM SIGKDD Explorations Newsletter
A Study of Hierarchical and Flat Classification of Proteins
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computational Biology and Chemistry
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
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Protein fold recognition with combined SVM-RDA classifier
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Computers in Biology and Medicine
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
A novel approach to protein structure prediction using PCA or LDA based extreme learning machines
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Online learning with multiple kernels: A review
Neural Computation
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
Protein fold recognition with a two-layer method based on SVM-SA, WP-NN and C4.5 TLM-SNC
International Journal of Data Mining and Bioinformatics
Enhancing protein fold prediction accuracy using evolutionary and structural features
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Comparing ensemble learning methods based on decision tree classifiers for protein fold recognition
International Journal of Data Mining and Bioinformatics
| Hi-index | 3.84 |
Motivation: Prediction of protein folding patterns is one level deeper than that of protein structural classes, and hence is much more complicated and difficult. To deal with such a challenging problem, the ensemble classifier was introduced. It was formed by a set of basic classifiers, with each trained in different parameter systems, such as predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability, as well as different dimensions of pseudo-amino acid composition, which were extracted from a training dataset. The operation engine for the constituent individual classifiers was OET-KNN (optimized evidence-theoretic k-nearest neighbors) rule. Their outcomes were combined through a weighted voting to give a final determination for classifying a query protein. The recognition was to find the true fold among the 27 possible patterns. Results: The overall success rate thus obtained was 62% for a testing dataset where most of the proteins have Availability: The ensemble classifier, called PFP-Pred, is available as a web-server at http://202.120.37.186/bioinf/fold/PFP-Pred.htm for public usage. Contact: lifesci-sjtu@san.rr.com Supplementary information: Supplementary data are available on Bioinformatics online.