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Stratification of admixture population:A bayesian approach

Tamiji, M ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1109/CFIS.2019.8692151
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. A statistical algorithm is introduced to improve the false inference of active loci, in the population in which members are admixture. The algorithm uses an advanced clustering algorithm based on a Bayesian approach. The proposed algorithm simultaneously infers the hidden structure of the population. In this regard, the Monte Carlo Markov Chain (MCMC) algorithm has been used to evaluate the posterior probability distribution of the model parameters. The proposed algorithm is implemented in a bundle, and then its performance is widely evaluated in a number of artificial databases. The accuracy of the clustering algorithm is compared with the STRUCTURE method based on certain criterion. © 2019 IEEE
  6. Keywords:
  7. Admixture Populations ; Probabilistic Graphical Model ; Bayesian networks ; Bioinformatics ; Inference engines ; Intelligent systems ; Markov processes ; Population statistics ; Probability distributions ; Bayesian approaches ; Hidden structures ; Model parameters ; Monte Carlo Markov chain ; Population stratification ; Probabilistic graphical models ; Statistical algorithm ; Clustering algorithms
  8. Source: 7th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2019, 29 January 2019 through 31 January 2019 ; 2019 ; 9781728106731 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/8692151