TY - GEN
T1 - Protein structure classification using local Hölder exponents estimated by wavelet transform
AU - Zhou, Yu
AU - Yu, Zu Guo
AU - Anh, Vo
PY - 2008
Y1 - 2008
N2 - In this paper we use local Holder exponents to capture local patterns in protein sequences. The numerical sequence of a protein based on a 6-letters model of amino acids is considered as a time series, then its local Hölder exponents are estimated using the wavelet transform. The probability density of local Hölder exponents is then calculated. The probability density values are then taken as features for a perceptron constructed by Neural Network Toolbox in Matlab to classify proteins from the all-α, all-β, α + β and α/β protein structure classes. Numerical results indicate that all selected large proteins can be classified with 100% accuracies.
AB - In this paper we use local Holder exponents to capture local patterns in protein sequences. The numerical sequence of a protein based on a 6-letters model of amino acids is considered as a time series, then its local Hölder exponents are estimated using the wavelet transform. The probability density of local Hölder exponents is then calculated. The probability density values are then taken as features for a perceptron constructed by Neural Network Toolbox in Matlab to classify proteins from the all-α, all-β, α + β and α/β protein structure classes. Numerical results indicate that all selected large proteins can be classified with 100% accuracies.
UR - https://www.scopus.com/pages/publications/57649166887
U2 - 10.1109/ICNC.2008.296
DO - 10.1109/ICNC.2008.296
M3 - 会议稿件
AN - SCOPUS:57649166887
SN - 9780769533049
T3 - Proceedings - 4th International Conference on Natural Computation, ICNC 2008
SP - 104
EP - 108
BT - Proceedings - 4th International Conference on Natural Computation, ICNC 2008
T2 - 4th International Conference on Natural Computation, ICNC 2008
Y2 - 18 October 2008 through 20 October 2008
ER -