HMM in predicting protein secondary structure |
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Authors: | Huang Jing Shi Feng Zou Xiu-fen Li Yuan-xiang Zhou Huai-bei |
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Institution: | (1) School of Mathematics and Statistics, Wuhan University, 430072 Wuhan, Hubei, China;(2) Advanced Research Center for Science & Technology, Wuhan University, 430072 Wuhan, Hubei, China;(3) State Key Laboratory of Software Engineer, Wuhan University, 430072 Wuhan, Hubei, China |
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Abstract: | We introduced a new method—duration Hidden Markov Model (dHMM) to predicate the secondary structure of Protein. In our study,
we divide the basic second structure of protein into three parts: H (α-Helix), E (β-sheet) and O (others, include coil and
turn). HMM is a kind of probabilistic model which more thinking of the interaction between adjacent amino acids (these interaction
were represented by transmit probability), and we use genetic algorithm to determine the model parameters. After improving
on the model and fixed on the parameters of the model, we write a program HMMPS. Our example shows that HMM is a nice method
for protein secondary structure prediction.
Foundation item: Supported by the National Natural Science Foundation of China (30170214)
Biography: Huang Jing (1977-), female, Master candidate, research direction: bioinformatics. |
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Keywords: | hidden markov Model Viterbi algorithm protein secondary structure |
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