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Analyzing gene expression time-courses based on multi-resolution shape mixture model.

Ying Li1, Ye He1, Yu Zhang1

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun 130012, China.

Mathematical Biosciences
|September 14, 2016
PubMed
Summary
This summary is machine-generated.

We developed a new clustering method using fractal features and mixture models to analyze time-course gene expression data. This approach identifies biologically significant gene groups more effectively than existing methods.

Keywords:
Bayesian information criterionGlobal fractal scaleLocal fractal scaleMixture model clusteringMulti-resolution fractal feature

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Biological processes are dynamic molecular events.
  • Time-course gene expression experiments are crucial for understanding biological development and disease progression.
  • Analyzing time-course gene expression profiles remains a challenge.

Purpose of the Study:

  • To propose a novel shape-based mixture model clustering method for gene expression time-course profiles.
  • To explore significant gene groups within dynamic biological processes.
  • To provide an alternative tool for analyzing time-course gene expression.

Main Methods:

  • Developed a multi-resolution shape mixture model algorithm.
  • Utilized multi-resolution fractal features computed by wavelet decomposition.
  • Applied a probabilistic framework for robust clustering of time-course gene expression.

Main Results:

  • The proposed algorithm demonstrated strong biological significance in grouping genes from yeast time-course expression profiles.
  • Compared favorably against several popular clustering methods.
  • Identified gene groups with greater biological relevance through pathway enrichment and protein-protein interaction analysis.

Conclusions:

  • A novel multi-resolution shape mixture model algorithm based on fractal features was successfully developed.
  • The proposed model offers a new perspective and tool for the visualization and analysis of time-course gene expression data.