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应用科学学报 ›› 2010, Vol. 28 ›› Issue (3): 307-312.

• 信号与信息处理 • 上一篇    下一篇

二阶HMM算法改进及在miRNA靶基因预测中的应用

高松， 秦殿刚， 冯铁男， 马成荣， 王翼飞   

  1. 上海大学数学系，上海200444
  • 收稿日期:2009-12-10 修回日期:2010-04-23 出版日期:2010-05-21 发布日期:2010-05-21
  • 作者简介:王翼飞，教授，博导，研究方向：计算分子生物学，E-mail: yifei_wang@staff.shu.edu.cn
  • 基金资助:

    国家自然科学基金(No.30871341)；上海市重点学科基金(No.S30104)；上海市教委重点学科建设项目基金(No.J50101)；科技部重大科技专项基金(No.2008ZX10002-017, No.2008ZX10002-020, No.2009ZX09103-686)资助

Improved Algorithms of HMM2 and Applications to MiRNA Target Predictions

GAO Song, QIN Dian-gang, FENG Tie-nan, MA Cheng-rong, WANG Yi-fei   

  1. Department of Mathematics, Shanghai University, Shanghai 200444, China
  • Received:2009-12-10 Revised:2010-04-23 Online:2010-05-21 Published:2010-05-21

PDF

2189

被引次数

7

摘要：

隐马氏模型在语音识别和生物信息学中有重要的应用. 本文研究二阶隐马氏模型(HMM2)的基本算法，利用归一化和递推原理，改进模型的前向-后向算法及Baum-Welch训练算法并给予证明，使得该算法更容易理解和机器实现，并保证数值稳定性. 将HMM2应用到miRNA靶基因预测的后期过滤处理中取得了较好的结果.

关键词: 二阶隐马氏模型, 前向-后向算法, Baum-Welch算法, miRNA靶基因

Abstract:

The hidden Markov model has important applications in speech recognition and bioinformatics. This paper studies basic algorithms of the second-order hidden Markov model (HMM2), improves the forward-backward algorithm and Baum-Welch training algorithm of the model. We provide the proof using normalization and recursion, making them easier to be understood and implemented in programming, and ensuring numerical stability. The HMM2 is applied to miRNA target predictions of post-processing filters with good results.

Key words: second-order hidden Markov model (HMM2), forward-backward algorithm, Baum-Welch algorithm, miRNA target gene

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