Machine Learning
Machine Learning
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Counter-Example Generation-Based One-Class Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Propositionalisation of Profile Hidden Markov Models for Biological Sequence Analysis
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
A One-Class Classification Approach for Protein Sequences and Structures
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
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Profile Hidden Markov Models (PHMMs) have been widely used as models for Multiple Sequence Alignments. By their nature, they are generative one-class classifiers trained only on sequences belonging to the target class they represent. Nevertheless, they are often used to discriminate between classes. In this paper, we investigate the beneficial effects of information from non-target classes in discriminative tasks. Firstly, the traditional PHMM is extended to a new binary classifier. Secondly, we propose propositional representations of the original PHMM that capture information from target and non-target sequences and can be used with standard binary classifiers. Since PHMM training is time intensive, we investigate whether our approach allows the training of the PHMM to stop, before it is fully converged, without loss of predictive power.