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1. www.nature.com › articles › s41592/019/0476-xMarkov models—Markov chains - Nature Methods

   www.nature.com › articles › s41592/019/0476-x

   Jul 30, 2019 · Discrete-time Markov chains are intuitive, are easy to interpret and have real-life applications in various areas, including weather prediction, finance and biology.

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3. www.ncbi.nlm.nih.gov › pmc › articlesHidden Markov Models and their Applications in Biological ...

   www.ncbi.nlm.nih.gov › pmc › articles

   Hidden Markov models (HMMs) have been extensively used in biological sequence analysis. In this paper, we give a tutorial review of HMMs and their applications in a variety of problems in molecular biology. We especially focus on three types of HMMs: the profile-HMMs, pair-HMMs, and context-sensitive HMMs.

   • Author: Byung-Jun Yoon
   • DOI: 10.2174/138920209789177575

   • Publish Year: 2009
   • Publication: Curr Genomics. 2009 Sep; 10(6): 402-415.

4. bio.libretexts.org › Bookshelves › Computational7.3: Markov Chains and HMMS - From Example to Formalizing

   bio.libretexts.org › Bookshelves › Computational

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   Mar 17, 2021 · Markov Chains. A Markov Chain is given by a finite set of states and transition probabilities between the states. At every time step, the Markov Chain is in a particular state and undergoes a transition to another state.

5. ocw.mit.edu › courses › 6-047-computational-biologyLecture 4 Modeling Biological Sequences using Hidden Markov ...

   ocw.mit.edu › courses › 6-047-computational-biology

   Definition: A Markov chain is a triplet (Q, p, A), where: Q is a finite set of states. Each state corresponds to a symbol in the alphabet Σ. p is the initial state probabilities. A is the state transition probabilities, denoted by ast for each s, t in Q.

6. towardsdatascience.com › hidden-markov-modelHidden Markov Model for Biological Sequence | by Mattia ...

   towardsdatascience.com › hidden-markov-model

   Dec 13, 2020 · The Markov Chains and the Hidden Markov Model are powerful statistical models that can be applied in a variety of different fields, such as protein homologies detection; speech recognition; language processing; telecommunications; and tracking animal behaviour.

7. genetics.bwh.harvard.edu › courses › Biophysics205_2020What is a hidden Markov model? - Harvard University

   genetics.bwh.harvard.edu › courses › Biophysics205_2020

   Statistical models called hidden Markov models are a recurring theme in computational biology. What are hidden Markov models, and why are they so useful for so many different problems? Often, biological sequence analysis is just a matter of putting the right label on each residue.

8. People also ask

   Are hidden Markov models used in Biological Sequence analysis?

   • Until now, various signal processing models and algorithms have been used in biological sequence analysis, among which the hidden Markov models (HMMs) have been especially popular.

   Hidden Markov Models and their Applications in Biological Sequence A…

   www.ncbi.nlm.nih.gov/pmc/articles/PMC2766791/

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   How to adapt the Markov model to the study of sequences?

   • To adapt the Markov Model to the study of sequences, the concept of Profile HMM has been introduced. The Profile HMM is a variation of the Hidden Markov Chain in which the position of a multiple sequence alignment becomes the states of the model; the transition matrix is the probability to pass from one state/position to the next.

   Hidden Markov Model for Biological Sequence | by Mattia Cinelli

   towardsdatascience.com/hidden-markov-model-applied-to-biological-sequence-373d2b5a24c

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   How does a Markov chain work?

   • A Markov Chain is given by a finite set of states and transition probabilities between the states. At every time step, the Markov Chain is in a particular state and undergoes a transition to another state.

   7.3: Markov Chains and HMMS - From Example to Formalizing

   bio.libretexts.org/Bookshelves/Computational_Biology/Book:_Computational_Biology_-_Genomes_Networks_and_Evolution_(Kellis_et_al.)/07:_Hidden_Markov_Models_I/7.03:_Markov_Chains_and_HMMS-_From_Example_to_Formalizing

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   What is the difference between a stochastic model and a Markov chain?

   • In computer science, Markov Chains are foundational to algorithms in machine learning, search engines, and data mining, notably in applications like Google’s PageRank algorithm which ranks web pages based on their link structure. Stochastic models are mathematical models that incorporate randomness.

   Markov Chains: A Comprehensive Guide to Stochastic Processes ... - Me…

   medium.com/data-and-beyond/markov-chains-a-comprehensive-guide-to-stochastic-processes-and-the-chapman-kolmogorov-equation-8aa04d1e0349

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9. medium.com › data-and-beyond › markov-chains-aMarkov Chains: A Comprehensive Guide to Stochastic ... - Medium

   medium.com › data-and-beyond › markov-chains-a

   Feb 19, 2024 · In computer science, Markov Chains are foundational to algorithms in machine learning, search engines, and data mining, notably in applications like Google’s PageRank algorithm which ranks web...