The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Neural Networks and Genome Informatics
Neural Networks and Genome Informatics
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Fast learning in networks of locally-tuned processing units
Neural Computation
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Fast nonnegative matrix factorization and its application for protein fold recognition
EURASIP Journal on Applied Signal Processing
Supervised machine learning algorithms for protein structure classification
Computational Biology and Chemistry
Protein fold recognition with adaptive local hyperplane algorithm
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Letters: Fusion of classifiers for protein fold recognition
Neurocomputing
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Some new features for protein fold prediction
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Application of classifier fusion for protein fold recognition
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
A Study of Hierarchical and Flat Classification of Proteins
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Margin-based ensemble classifier for protein fold recognition
Expert Systems with Applications: An International Journal
Using rotation forest for protein fold prediction problem: an empirical study
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Protein fold recognition with combined SVM-RDA classifier
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Finding short structural motifs for re-construction of proteins 3D structure
Applied Soft Computing
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
Comparing ensemble learning methods based on decision tree classifiers for protein fold recognition
International Journal of Data Mining and Bioinformatics
Boosting-SVM: effective learning with reduced data dimension
Applied Intelligence
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Classifying the structure of protein is a very important task in biological data. By means of the classification, the relationships and characteristics among known proteins can be exploited to predict the structure of new proteins. The study of the protein structures is based on the sequences and their similarity. It is a difficult task. Recently, due to the ability of machine learning techniques, many researchers have applied them to probe into this protein classification problem. We also apply here machine learning methods for multi-class protein fold recognition problem by proposing a novel hierarchical learning architecture. This novel hierarchical learning architecture can be formed by NN (neural networks) or SVM (support vector machine) as basic building blocks. Our results show that both of them can perform well. We use this new architecture to attack the multi-class protein fold recognition problem as proposed by Dubchak and Ding in 2001. With the same set of features our method can not only obtain better prediction accuracy and lower computation time, but also can avoid the use of the stochastic voting process in the original approach.