A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Proceedings of the sixth annual international conference on Computational biology
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Profile-Based String Kernels for Remote Homology Detection and Motif Extraction
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Protein homology detection with biologically inspired features and interpretable statistical models
International Journal of Data Mining and Bioinformatics
Protein homology detection with biologically inspired features and interpretable statistical models
International Journal of Data Mining and Bioinformatics
Modelling splice sites with locality-sensitive sequence features
International Journal of Data Mining and Bioinformatics
Biological Sequence Classification with Multivariate String Kernels
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Computational classification of proteins using methods such as string kernels and Fisher-SVM has demonstrated great success. However, the resulting models do not offer an immediate interpretation of the underlying biological mechanisms. In this work, we propose a biologically motivated feature set combined with a sparse classifier, based on a small subset of positions and residues in protein sequences, for protein superfamily detection and show the performance of our models is comparable to that of the state-of-the-art methods on a benchmark dataset. The set of sparse critical features discovered by the models is consistent with the confirmed biological findings.