SVM-BALSA: Remote Homology Detection based on Bayesian Sequence Alignment
Journal Article
·
· Computational Biology and Chemistry, 29(6):440-3
Using biopolymer sequence comparison methods to identify evolutionarily related proteins is one of the most common tasks in bioinformatics. Recently, support vector machines (SVMs) utilizing statistical learning theory have been employed in the problem of remote homology detection and shown to outperform iterative profile methods such as PSI-BLAST. In this study we demonstrate the utilization of a Bayesian alignment score, which accounts for the uncertainty of all possible alignments, in the SVM construction improves sensitivity compared to the traditional dynamic programming implementation.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 878675
- Report Number(s):
- PNNL-SA-45823
- Journal Information:
- Computational Biology and Chemistry, 29(6):440-3, Journal Name: Computational Biology and Chemistry, 29(6):440-3 Journal Issue: 6 Vol. 29
- Country of Publication:
- United States
- Language:
- English
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