Abstract
This paper studies the sensitivity of PSI-BLAST with respect to the 'h' parameter. Observing that the standard PSI-BLAST is sensitive to parameter 'h' in the high-value region, we propose a new technique, called Boosting PSI-BLAST, to reduce the sensitivity, By constraining 'h' to a small value first so as to reduce the chance of early corruption and then relaxing it gradually to increase divergence, the boosting PSI-BLAST not only can reduce the sensitivity to h-value, but also may strike a good balance between corruption and divergence in profiles. Tests on Reinhardt and Hubbard's eukaryotic protein dataset verify that our method is better in reducing the sensitivity of profile alignment scores to h-value than the standard PSI-BLAST.
Original language | English |
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Title of host publication | Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP |
Pages | 39-44 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 1 Dec 2007 |
Event | 17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007 - Thessaloniki, Greece Duration: 27 Aug 2007 → 29 Aug 2007 |
Conference
Conference | 17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007 |
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Country/Territory | Greece |
City | Thessaloniki |
Period | 27/08/07 → 29/08/07 |
ASJC Scopus subject areas
- General Computer Science
- Signal Processing