What is the expectation maximization algorithm?

@article{Do2008WhatIT,
  title={What is the expectation maximization algorithm?},
  author={Chuong B. Do and Serafim Batzoglou},
  journal={Nature Biotechnology},
  year={2008},
  volume={26},
  pages={897-899}
}
The expectation maximization algorithm arises in many computational biology applications that involve probabilistic models. What is it good for, and how does it work? 
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323 Citations
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323 Citations

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References

SHOWING 1-10 OF 12 REFERENCES
A modified expectation maximization algorithm for penalized likelihood estimation in emission tomography
  • A. Pierro
  • Mathematics
    IEEE Trans. Medical Imaging
  • 1995
TLDR
The new method is a natural extension of the EM for maximizing likelihood with concave priors for emission tomography and convergence proofs are given.
Testing for linkage disequilibrium in genotypic data using the Expectation-Maximization algorithm
TLDR
It is concluded that with highly polymorphic loci, the EM algorithm does lead to a useful test for linkage disequilibrium, but that it is necessary to find the empirical distribution of likelihood ratios in order to perform a test of significance correctly.
An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences
TLDR
Statistical methodology for the identification and characterization of protein binding sites in a set of unaligned DNA fragments is presented and the final motif is utilized in a search for undiscovered CRP binding sites.
Hidden Markov models in computational biology. Applications to protein modeling.
TLDR
The results suggest the presence of an EF-hand calcium binding motif in a highly conserved and evolutionary preserved putative intracellular region of 155 residues in the alpha-1 subunit of L-type calcium channels which play an important role in excitation-contraction coupling.
Genome-wide discovery of transcriptional modules from DNA sequence and gene expression
TLDR
The EM algorithm is used to identify transcriptional modules--sets of genes that are co-regulated in a set of experiments, through a common motif profile, and refines both the module assignment and the motif profile so as to best explain the expression data as a function of transcriptional motifs.
Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population.
TLDR
An expectation-maximization (EM) algorithm leading to maximum-likelihood estimates of molecular haplotype frequencies under the assumption of Hardy-Weinberg proportions is implemented and appears to be useful for the analysis of nuclear DNA sequences or highly variable loci.
RNA sequence analysis using covariance models.
We describe a general approach to several RNA sequence analysis problems using probabilistic models that flexibly describe the secondary structure and primary sequence consensus of an RNA sequence…
THE ESTIMATION OF GENE FREQUENCIES IN A RANDOM‐MATING POPULATION
TLDR
This method is applied to data on blood groups collected from villages near the mouth of the River Po, in northern Italy, in the course of an investigation on microcythaemia, and it is shown to be equivalent to maximum likelihood, and therefore fully efficient in the statistical sense.
Maximum Likelihood Estimation from Incomplete Data
A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
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