Mining p53 binding sites using profile hidden Markov model

被引:2
作者
Huang, J [1 ]
Li, SJ [1 ]
机构
[1] Indiana Univ Purdue Univ, Dept Comp & Informat Sci, Indianapolis, IN 46202 USA
来源
ITCC 2005: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY: CODING AND COMPUTING, VOL 1 | 2005年
关键词
D O I
10.1109/ITCC.2005.197
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Hidden Markov Model has been successfully applied to bacterial gene finders and mRNA splicing modeling. Using a set of observing DNA sequences, HMM is derived for homologous search. In this paper we develop profile HMM in detecting p53, a tumor suppressor, binding sites along genes. Without assuming the constant number of nucleotides in p53 binding site, profile HMM and viterbi algorithms are designed to detect the embedded p53 binding sites from the promoter genes chosen from GenBank. The p53 regulated genes containing either single or multiple p53 binding sites distributed as clusters can be identified and classified into 7 functional groups including cell cycle regulation, DNA damage repair, signaling transduction, transcriptional factor, stress response, tumor suppressor, and oncogen.
引用
收藏
页码:146 / 151
页数:6
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