A Modified Bidirectional Hidden Markov Model and its Application in Protein Secondary Structure Prediction

摘要：

A hidden Markov model (HMM) is a statistical tool applied to model stochastic sequences. In an ordinary HMM a hidden Markov chain named, the chain of states emits a sequence of observations. In this model, given a state the emissions are assumed to be independent from each other. However several researchers have already studied the dependencies bttween emissions.In this paper a new approach for consideration of dependencies among emissions is presented. We start with the use of the information of the left hand side of any emission and introduce a new model. We then take the information of the right hand side of any emission into account Protein is one of the most important molecules in any living cells and the study of protein structure is very important in biology. Predicting the secondary structure of a protein is usually used for the 3D structure prediction of it which in turn helps to identify a protein structure in whole. In this paper we discuss a two-sided modified HMM considering some dependencies among emissions. This model construction seems to be reasonable and improves the precision of protein secondary structure prediction.

关键词： Hidden Markov Models Protein Secondary Structure Left-to-Right and Right-to-Left Dependency Model Posterior Decoding Viterbi Algorithm

作者: Hamid Pezeshk Sima Naghizadeh Seyed Amir Malekpour Changiz Eslahchi Mehdi Sadeghi

作者单位: Corresponding author,School of Mathematics, Statistics and Computer Science and Center of Excellence Department of Statistics, Tarbiat Modarres University, Tehran, Iran Faculty of Mathematics, Shahid Beheshti University, Tehran,Iran Department of Biophysics, National Institute of Genetic Engineering and Biotechnology, Tehran,Iran S

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 535-538

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）