LPC-VQ based hidden Markov models for similarity searching in DNA sequences
Pham, Tuan D., and Yan, Hong (2006) LPC-VQ based hidden Markov models for similarity searching in DNA sequences. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics 2006 (2), pp. 1654-1659. From: SMC '06 IEEE International Conference on Systems, Man, and Cybernetics 2006, 8-11 October 2006, Taipei, Taiwan.
PDF (Published Version)
Restricted to Repository staff only
Given a newly found gene of some particular genome and a database of sequences whose functions have been known, it must be very helpful if we can search through the database and identify those that are similar to the particular new sequence. The search results may help us to understand the functional role, regulation, and expression of the new gene by the inference from the similar database sequences. This is the task of any methods developed for biological database searching. In this paper we present a new application of the theories of linear predictive coding, vector quantization, and hidden Markov models to address the problem of DNA sequence similarity search where there is no need for sequence alignment. The proposed approach has been tested and compared with some existing methods against real DNA and genomic datasets. The experimental results demonstrate its potential use for such purpose.
|Item Type:||Conference Item (Refereed Research Paper - E1)|
|Keywords:||DNA; similarity measure; hidden Markov models|
|Date Deposited:||23 Nov 2009 05:42|
|SEO Codes:||92 HEALTH > 9202 Health and Support Services > 920203 Diagnostic Methods @ 34%
92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920412 Preventive Medicine @ 33%
92 HEALTH > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920102 Cancer and Related Disorders @ 33%
|Citation Count from Web of Science||