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CLICK: a clustering algorithm with applications to gene expression analysis.

R Sharan1, R Shamir

  • 1Department of Computer Science, Tel-Aviv University, Israel. roded@math.tau.ac.il

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|September 8, 2000
PubMed
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We developed CLICK, a novel clustering algorithm for analyzing gene expression data. CLICK efficiently identifies gene groups with similar patterns, outperforming existing methods in speed and accuracy.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • DNA microarrays allow simultaneous monitoring of thousands of gene expression levels.
  • Analyzing gene expression data necessitates clustering genes with similar expression patterns.

Purpose of the Study:

  • To introduce CLICK, a novel clustering algorithm for gene expression analysis.
  • To apply CLICK to various biological datasets, including gene expression, cDNA oligo-fingerprinting, and protein sequence similarity.

Main Methods:

  • CLICK utilizes graph-theoretic and statistical techniques to identify tight groups of similar elements (kernels).
  • Heuristic procedures are employed to expand kernels into complete clusters.
  • The algorithm makes no prior assumptions on cluster structure or number.

Related Experiment Videos

Main Results:

  • CLICK demonstrated superior performance compared to existing algorithms across multiple biological applications.
  • The algorithm achieved high accuracy in identifying clusters of genes with similar expression patterns.
  • CLICK is highly efficient, clustering thousands of elements in minutes and over 100,000 in hours.

Conclusions:

  • CLICK is a novel, fast, and accurate clustering algorithm suitable for gene expression analysis and other biological applications.
  • The algorithm's flexibility and performance make it a valuable tool for large-scale biological data analysis.