Difference between revisions of "A Comparative Analysis of Protein Homology Detection Methods"
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| − | Functional annotation of new gene sequences is an important challenge for computational biology systems. While much progress has been made towards improving experimental methods for functional assignment to putative genes, most current genomic annotation methods rely on computational solutions for homology modeling via sequence or structural similarity. With the increasing number of computer methods available for protein remote homologies detection, a comparative evaluation of the methods from biological prospective is warranted. This study uses benchmark SCOP dataset to test and compare the ability of five different computational methods for protein homologies detection. The results provide insight to biologist as to usage, value, and reliability of the numerous methods available.[[Category:Scientific Paper]] | + | Functional annotation of new gene sequences is an important challenge for computational biology systems. While much progress has been made towards improving experimental methods for functional assignment to putative genes, most current genomic annotation methods rely on computational solutions for homology modeling via sequence or structural similarity. With the increasing number of computer methods available for protein remote homologies detection, a comparative evaluation of the methods from biological prospective is warranted. This study uses benchmark SCOP dataset to test and compare the ability of five different computational methods for protein homologies detection. The results provide insight to biologist as to usage, value, and reliability of the numerous methods available. |
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| + | [[Category:Scientific Paper|comparative analysis protein homology detection methods]] | ||
Latest revision as of 09:53, 1 January 2017
| Scientific Paper | |
|---|---|
| Title | A Comparative Analysis of Protein Homology Detection Methods |
| Read in full | Link to paper |
| Author(s) | Nazar M Zaki |
| Keywords | Homology detection, hidden Marko model, protein classification, support vector machines |
| Published | 2003 |
| Journal | Journal of Theoretics |
| Volume | 5 |
| Number | 4 |
| No. of pages | 7 |
Read the full paper here
Abstract
Functional annotation of new gene sequences is an important challenge for computational biology systems. While much progress has been made towards improving experimental methods for functional assignment to putative genes, most current genomic annotation methods rely on computational solutions for homology modeling via sequence or structural similarity. With the increasing number of computer methods available for protein remote homologies detection, a comparative evaluation of the methods from biological prospective is warranted. This study uses benchmark SCOP dataset to test and compare the ability of five different computational methods for protein homologies detection. The results provide insight to biologist as to usage, value, and reliability of the numerous methods available.
