Profile Based Direct Kernels for Remote Homology Detection and Fold Recognition
Huzefa Rangwala and George Karypis |
Bioinformatics, Vol. 31, No. 23, pp. 4239 - 4247, 2005 |
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Abstract Motivation: Remote homology detection between protein sequences is a central problem in computational biology. Supervised learning algorithms based on support vector machines are currently the most effective method for remote homology detection. The performance of these methods depends on how the protein sequences are modeled and on the method used to compute the kernel function between them.
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Research topics: Bioinformatics | Classification | Data mining | Protein structure prediction |