TY - GEN
T1 - Sensitivity analysis of boosting PSI-BLAST with case study on subcellular localization
AU - Mai, F.
AU - Mak, M. W.
AU - Hung, Y. S.
AU - Kung, S. Y.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=48149087990&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=48149087990&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2007.4414279
DO - 10.1109/MLSP.2007.4414279
M3 - Conference contribution
AN - SCOPUS:48149087990
SN - 1424415667
SN - 9781424415663
T3 - Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
SP - 39
EP - 44
BT - Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
T2 - 17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007
Y2 - 27 August 2007 through 29 August 2007
ER -