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Related Experiment Video

Updated: Jun 13, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

Efficient and exact maximum likelihood quantisation of genomic features using dynamic programming.

Mingzhou Song1, Robert M Haralick, Stéphane Boissinot

  • 1Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA. joemsong@cs.nmsu.edu

International Journal of Data Mining and Bioinformatics
|April 29, 2010
PubMed
Summary
This summary is machine-generated.

A novel dynamic programming algorithm precisely quantizes continuous variables into discrete ones, maximizing distribution likelihood. This method enhances genomic analysis, outperforming k-means clustering for data discretization.

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Computational Biology
  • Genomics
  • Statistical Modeling

Background:

  • Continuous variables often require discretization for statistical analysis and modeling.
  • Existing methods like k-means clustering can be inexact and distance-based.
  • Accurate discretization is crucial for analyzing complex biological data, such as genomic features.

Purpose of the Study:

  • To introduce an efficient and exact dynamic programming algorithm for quantizing continuous random variables into discrete ones.
  • To maximize the likelihood of the quantized probability distribution for the original continuous random variable.
  • To apply and evaluate this algorithm on genomic data, specifically recombination rates and transposable elements.

Main Methods:

  • Development of an exact dynamic programming algorithm for quantizing continuous data.
  • Application of the algorithm to genomic features: recombination rate and LINE-1 transposable element length.
  • Comparative analysis against the univariate iterative k-means clustering algorithm.

Main Results:

  • The dynamic programming algorithm provides an exact and density-preserving quantisation.
  • Successful application to genomic data, enabling analysis of recombination rates around LINE-1 elements.
  • Demonstrated superiority over the k-means clustering algorithm for discretization tasks.

Conclusions:

  • The proposed dynamic programming approach offers a superior alternative to k-means for discretizing continuous genomic data.
  • This exact quantisation method enhances the accuracy of statistical analysis in genomics.
  • The algorithm facilitates a deeper understanding of associations between genomic features like recombination and transposable elements.