Extracting Gene Regulation Information from Microarray Time-Series Data Using Hidden Markov Models

2006 ◽  
pp. 144-153 ◽  
Author(s):  
Osman N. Yoğurtçu ◽  
Engin Erzin ◽  
Attila Gürsoy
Keyword(s):  
Time Series ◽  
Gene Regulation ◽  
Hidden Markov Models ◽  
Time Series Data ◽  
Markov Models ◽  
Hidden Markov ◽  
Series Data

Use of unlabeled time series data in hidden Markov models

2003 ◽  
Vol 34 (13) ◽  
pp. 1-12
Author(s):  
Masashi Inoue ◽  
Naonori Ueda
Keyword(s):  
Time Series ◽  
Hidden Markov Models ◽  
Time Series Data ◽  
Markov Models ◽  
Hidden Markov ◽  
Series Data

scholarly journals A Hidden Markov Model for the Linguistic Analysis of the Voynich Manuscript

2019 ◽  
Vol 24 (1) ◽  
pp. 14 ◽  
Author(s):  
Luis Acedo
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Mathematical Linguistics ◽  
Underlying Structure ◽  
Internal States ◽  
Consonant Sounds

Hidden Markov models are a very useful tool in the modeling of time series and any sequence of data. In particular, they have been successfully applied to the field of mathematical linguistics. In this paper, we apply a hidden Markov model to analyze the underlying structure of an ancient and complex manuscript, known as the Voynich manuscript, which remains undeciphered. By assuming a certain number of internal states representations for the symbols of the manuscripts, we train the network by means of the α and β -pass algorithms to optimize the model. By this procedure, we are able to obtain the so-called transition and observation matrices to compare with known languages concerning the frequency of consonant andvowel sounds. From this analysis, we conclude that transitions occur between the two states with similar frequencies to other languages. Moreover, the identification of the vowel and consonant sounds matches some previous tentative bottom-up approaches to decode the manuscript.

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