Maximum likelihood quantization of genomic features using dynamic programming

Mingzhou Song, Robert M. Haralick, Stéphane Boissinot

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Dynamic programming is introduced to quantize a continuous random variable into a discrete random variable. Quantization is often useful before statistical analysis or reconstruction of large network models among multiple random variables. The quantization, through dynamic programming, finds the optimal discrete representation of the original probability density function of a random variable by maximizing the likelihood for the observed data. This algorithm is highly applicable to study genomic features such as the recombination rate across the chromosomes and the statistical properties of non-coding elements such as LINEI. In particular, the recombination rate obtained by quantization is studied for LINEI elements that are grouped also using quantization by length. The exact and density-preserving quantization approach provides an alternative superior to the inexact and distance-based k-means clustering algorithm for discretization of a single variable.

Original languageEnglish (US)
Title of host publicationProceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007
Pages547-553
Number of pages7
DOIs
StatePublished - 2007
Event6th International Conference on Machine Learning and Applications, ICMLA 2007 - Cincinnati, OH, United States
Duration: Dec 13 2007Dec 15 2007

Publication series

NameProceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007

Other

Other6th International Conference on Machine Learning and Applications, ICMLA 2007
CountryUnited States
CityCincinnati, OH
Period12/13/0712/15/07

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction
  • Control and Systems Engineering

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