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Related Experiment Video

Updated: Jun 21, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Curve-based clustering of time course gene expression data using self-organizing maps.

Xin Chen1

  • 1Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. chenxin@ntu.edu.sg

Journal of Bioinformatics and Computational Biology
|July 28, 2009
PubMed
Summary
This summary is machine-generated.

A new clustering algorithm, CurveSOM, accurately groups time course gene expression data by analyzing temporal correlations. This tool aids in discovering gene relationships and inferring biological pathways.

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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Last Updated: Jun 21, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Clustering time course gene expression data is crucial for understanding biological processes.
  • Existing algorithms face challenges in accurately capturing temporal dynamics and inter-gene relationships.

Purpose of the Study:

  • To develop a novel clustering algorithm, CurveSOM, for time course gene expression data.
  • To enhance the accuracy of gene clustering by considering temporal correlations.
  • To facilitate the inference of biological pathways and regulatory networks.

Main Methods:

  • Representing each gene's expression profile using cubic smoothing splines.
  • Applying a self-organizing map (SOM)-based clustering approach to these splines.
  • Evaluating CurveSOM on yeast cell cycle datasets and comparing it with existing tools.

Main Results:

  • CurveSOM demonstrates high accuracy in grouping genes into clusters.
  • The algorithm effectively identifies time-shifted correlations in gene expression patterns across clusters.
  • Performance comparison shows CurveSOM as a promising alternative to established methods.

Conclusions:

  • CurveSOM is an effective tool for exploratory analysis of time course gene expression data.
  • The algorithm's ability to capture temporal dynamics and inter-cluster relationships aids biological discovery.
  • CurveSOM advances the field of gene expression data analysis.