A Hybrid Algorithm Based on Artificial Immune System and Hidden Markov Model for Multiple Sequence Alignment
Authors
Hongwei Ge1, Weimin Zhong, Wenli Du, Feng Qian, Lu Wang
1Automation Institute, East China University of Science and Technology, State-Key Laboratory of Chemical Engineering
Corresponding Author
Hongwei Ge
Available Online October 2007.
- DOI
- 10.2991/iske.2007.162How to use a DOI?
- Keywords
- Artificial immune system, hidden Markov model, multiple sequence alignment
- Abstract
Multiple sequence alignment (MSA) has become an essential tool in the analysis of biologic sequences. In this paper, an artificial immune system (AIS) is proposed to train hidden Markov models (HMMs). Further, an integration algorithm based on the HMM and AIS for the MSA is constructed and a decoding algorithm based on Viterbi algorithm is also proposed. The approach is tested on a set of standard instances taken from the benchmark alignment database, BAliBASE. Numerical results are compared with those obtained by using the Baum-Welch training algorithm. The results show that the proposed algorithm not only improves the alignment abilities, but also reduces the time cost.
- Copyright
- © 2007, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Hongwei Ge AU - Weimin Zhong AU - Wenli Du AU - Feng Qian AU - Lu Wang PY - 2007/10 DA - 2007/10 TI - A Hybrid Algorithm Based on Artificial Immune System and Hidden Markov Model for Multiple Sequence Alignment BT - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007) PB - Atlantis Press SP - 952 EP - 958 SN - 1951-6851 UR - https://doi.org/10.2991/iske.2007.162 DO - 10.2991/iske.2007.162 ID - Ge2007/10 ER -