Family-based homology detection via pairwise sequence comparison
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An introduction to support Vector Machines: and other kernel-based learning methods
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Using the Fisher Kernel Method to Detect Remote Protein Homologies
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ECML '02 Proceedings of the 13th European Conference on Machine Learning
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PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
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CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
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ICML '05 Proceedings of the 22nd international conference on Machine learning
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ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
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TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
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MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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One key element in understanding the molecular machinery of the cell is to understand the meaning, or function, of each protein encoded in the genome. A very successful means of inferring the function of a previously unannotated protein is via sequence similarity with one or more proteins whose functions are already known. Currently, one of the most powerful such homology detection methods is the SVM-Fisher method of Jaakkola, Diekhans and Haussler (ISMB 2000). This method combines a generative, profile hidden Markov model (HMM) with a discriminative classification algorithm known as a support vector machine (SVM). The current work presents an alternative method for SVM-based protein classification. The method, SVM-pairwise, uses a pairwise sequence similarity algorithm such as Smith-Waterman in place of the HMM in the SVM-Fisher method. The resulting algorithm, when tested on its ability to recognize previously unseen families from the SCOP database, yields significantly better remote protein homology detection than SVM-Fisher, profile HMMs and PSI-BLAST.