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Clustering high throughput biological data with B-MST, a minimum spanning tree based heuristic.

Harun Pirim1, Burak Ekşioğlu2, Andy D Perkins3

  • 1Department of Systems Engineering, King Fahd University of Petroleum and Minerals, 31261, KSA.

Computers in Biology and Medicine
|April 28, 2015
PubMed
Summary

This study introduces B-MST, a novel clustering algorithm for bioinformatics that uses a minimum spanning tree and a tightness and separation index (TSI) to find biologically relevant gene expression patterns.

Keywords:
Biological networksClusteringGene expression dataGraph miningHeuristics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Interpreting high-dimensional biological data, like gene expression from microarrays, presents significant bioinformatics challenges.
  • Clustering is a crucial initial step for analyzing such complex datasets.
  • Existing clustering methods require efficiency, reliability, and the ability to identify biologically meaningful groups.

Purpose of the Study:

  • To develop a novel clustering algorithm for improved interpretation of high-throughput biological data.
  • To introduce a new objective function, the tightness and separation index (TSI), for guiding the clustering process.
  • To enhance the biological relevance of clusters derived from gene expression data.

Main Methods:

  • A minimum spanning tree (MST) based heuristic algorithm, termed B-MST, was developed.
  • The B-MST algorithm is guided by the novel tightness and separation index (TSI) objective function.
  • A local search procedure was implemented to minimize the TSI value, optimizing cluster quality.

Main Results:

  • The B-MST algorithm demonstrated effectiveness in identifying biologically meaningful clusters.
  • Performance was validated using adjusted rand index (ARI) on microarray data with known classes.
  • Gene Ontology (GO) annotations were used to assess cluster relevance for datasets lacking predefined labels.

Conclusions:

  • The proposed B-MST algorithm, utilizing TSI and co-expression network topology, offers an effective approach for biological data clustering.
  • This method enhances the reliability and biological interpretability of clustering results in bioinformatics.
  • B-MST provides a valuable tool for analyzing gene expression data and uncovering biological insights.