DocumentCode :
1764798
Title :
Autoregressive and Iterative Hidden Markov Models for Periodicity Detection and Solenoid Structure Recognition in Protein Sequences
Author :
Song, N.Y. ; Hong Yan
Author_Institution :
Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, China
Volume :
17
Issue :
2
fYear :
2013
fDate :
41334
Firstpage :
436
Lastpage :
441
Abstract :
Traditional signal processing methods cannot detect interspersed repeats and generally cannot handle nonstationary signals. In this paper, we propose a new method for periodicity detection in protein sequences to locate interspersed repeats. We first apply the autoregressive model with a sliding window to find possible repeating subsequences within a protein sequence. Then, we utilize an iterative hidden Markov model (HMM) to count the number of subsequences similar to each of the possible repeating subsequences. An iterative HMM search of the potential repeating subsequences can help identify interspersed repeats. Finally, the numbers of repeating subsequences are aggregated together as a feature and used in the classification process. Experiment results show that our method improves the performance of solenoid protein recognition substantially.
Keywords :
autoregressive processes; hidden Markov models; iterative methods; molecular biophysics; molecular configurations; proteins; solenoids; autoregressive model; iterative HMM; iterative hidden Markov model; periodicity detection; protein sequence; solenoid protein recognition; solenoid structure recognition; Amino acids; Hidden Markov models; Mathematical model; Protein engineering; Protein sequence; Solenoids; Autoregressive (AR) model; Markov model; feature extraction; solenoid protein recognition; spectral analysis; Algorithms; Amino Acid Sequence; Computational Biology; Markov Chains; Models, Molecular; Molecular Sequence Data; Protein Conformation; Proteins; Sequence Analysis, Protein;
fLanguage :
English
Journal_Title :
Biomedical and Health Informatics, IEEE Journal of
Publisher :
ieee
ISSN :
2168-2194
Type :
jour
DOI :
10.1109/JBHI.2012.2235852
Filename :
6392183
Link To Document :
بازگشت