Finite Markov Chain Embedding for the Exact Distribution of Patterns in a Set of Random Sequences

Abstract : Patterns with “unusual” frequencies are new functional candidate patterns. Their identification is usually achieved by considering an homogeneous m-order Markov model (m≥ 1) of the sequence, allowing the computation of p-values. For practical reasons, stationarity of the model is often assumed. This approximation can result in some artifacts especially when a large set of small sequences is considered. In this work, an exact method, able to take into account both nonstationarity and fragmentary structure of sequences, is applied on a simulated and a real set of sequences. This illustrates that pattern statistics can be very sensitive to the stationary assumption. Keywords and phrases: stationary distribution - pattern Markov chain - biological patterns - finite Markov chain embedding
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Chapitre d'ouvrage
Advances in data analysis, Birkhäuser Boston, pp.171-180, 2010, 〈10.1007/978-0-8176-4799-5_16〉
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https://hal.archives-ouvertes.fr/hal-00539547
Contributeur : Grégory Nuel <>
Soumis le : mercredi 24 novembre 2010 - 15:44:35
Dernière modification le : mardi 10 octobre 2017 - 11:22:03

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Juliette Martin, Leslie Regad, Anne-Claude Camproux, Grégory Nuel. Finite Markov Chain Embedding for the Exact Distribution of Patterns in a Set of Random Sequences. Advances in data analysis, Birkhäuser Boston, pp.171-180, 2010, 〈10.1007/978-0-8176-4799-5_16〉. 〈hal-00539547〉

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