Combining pairwise sequence similarity and support vector machines for remote protein homology detection

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 SVMbased protein classification. The method, SVM-pairwise, uses a pairwise sequence similarity algorithm such as SmithWaterman 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 ...

• Type: Conference
• Year: 2002
• Where: RECOMB
• Authors: Li Liao, William Stafford Noble
• Keywords: Computational Biology, Computational Molecular Biology, RECOMB 2002, Sixth International Conference, Support Vector Machine