Literature DB >> 16646835

Statistical inference in evolutionary models of DNA sequences via the EM algorithm.

Asger Hobolth1, Jens Ledet Jensen.   

Abstract

We describe statistical inference in continuous time Markov processes of DNA sequences related by a phylogenetic tree. The maximum likelihood estimator can be found by the expectation maximization (EM) algorithm and an expression for the information matrix is also derived. We provide explicit analytical solutions for the EM algorithm and information matrix.

Year:  2005        PMID: 16646835     DOI: 10.2202/1544-6115.1127

Source DB:  PubMed          Journal:  Stat Appl Genet Mol Biol        ISSN: 1544-6115


  16 in total

1.  Counting labeled transitions in continuous-time Markov models of evolution.

Authors:  Vladimir N Minin; Marc A Suchard
Journal:  J Math Biol       Date:  2007-09-14       Impact factor: 2.259

2.  Learning to count: robust estimates for labeled distances between molecular sequences.

Authors:  John D O'Brien; Vladimir N Minin; Marc A Suchard
Journal:  Mol Biol Evol       Date:  2009-01-08       Impact factor: 16.240

3.  Relaxing the Molecular Clock to Different Degrees for Different Substitution Types.

Authors:  Hui-Jie Lee; Nicolas Rodrigue; Jeffrey L Thorne
Journal:  Mol Biol Evol       Date:  2015-04-29       Impact factor: 16.240

4.  Evaluation of Ancestral Sequence Reconstruction Methods to Infer Nonstationary Patterns of Nucleotide Substitution.

Authors:  Tomotaka Matsumoto; Hiroshi Akashi; Ziheng Yang
Journal:  Genetics       Date:  2015-05-06       Impact factor: 4.562

5.  Efficient maximum likelihood parameterization of continuous-time Markov processes.

Authors:  Robert T McGibbon; Vijay S Pande
Journal:  J Chem Phys       Date:  2015-07-21       Impact factor: 3.488

6.  Calculating Higher-Order Moments of Phylogenetic Stochastic Mapping Summaries in Linear Time.

Authors:  Amrit Dhar; Vladimir N Minin
Journal:  J Comput Biol       Date:  2017-02-08       Impact factor: 1.479

7.  Computational methods for birth-death processes.

Authors:  Forrest W Crawford; Lam Si Tung Ho; Marc A Suchard
Journal:  Wiley Interdiscip Rev Comput Stat       Date:  2018-01-02

8.  Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression.

Authors:  Yu-Ying Liu; Shuang Li; Fuxin Li; Le Song; James M Rehg
Journal:  Adv Neural Inf Process Syst       Date:  2015

9.  SIMULATION FROM ENDPOINT-CONDITIONED, CONTINUOUS-TIME MARKOV CHAINS ON A FINITE STATE SPACE, WITH APPLICATIONS TO MOLECULAR EVOLUTION.

Authors:  Asger Hobolth; Eric A Stone
Journal:  Ann Appl Stat       Date:  2009-09-01       Impact factor: 2.083

10.  Fitting and interpreting continuous-time latent Markov models for panel data.

Authors:  Jane M Lange; Vladimir N Minin
Journal:  Stat Med       Date:  2013-06-05       Impact factor: 2.373

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.