Protein secondary structure prediction
碩士 === 國立交通大學 === 生物科技研究所 === 91 === The prediction of secondary structure usually makes use of a sliding sequence window of a specific length (usually 9-15 amino acid residues) of a protein. In this work, we showed that a novel reduced representation of the input sequence vector can give...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2003
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| Online Access: | http://ndltd.ncl.edu.tw/handle/63962426823055802413 |
| Summary: | 碩士 === 國立交通大學 === 生物科技研究所 === 91 === The prediction of secondary structure usually makes use of a sliding sequence window of a specific length (usually 9-15 amino acid residues) of a protein. In this work, we showed that a novel reduced representation of the input sequence vector can give superior results to the existing method based on the usual binary representation of protein sequences. Our approach is based on the multi-class support vector machines that make use of reduced feature vectors consisting of the homology-weighted information of amino acids. Despite the relatively smaller size of our feature vectors, our approach gives prediction accuracy of 75%, which is better than the 73% of the well-known PHD approach.
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