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Updated: May 25, 2026

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish
11:42

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Published on: October 27, 2017

Gene expression analysis with integrated fuzzy C-means and pathway analysis.

Mingrui Zhang1, Beya Adamu, Chi-Cheng Lin

  • 1Computer Science Department, Winona State University, Winona, MN 55987, USA. MZhang@winona.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary

This study presents a Java software tool that links fuzzy clusters to biological pathways using a novel algorithm. It successfully classified lung cancer cell types, aiding gene association discovery.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Fuzzy clustering algorithms are used for gene expression data analysis.
  • Associating these clusters with biological pathways is crucial for interpretation.
  • Existing methods may lack efficiency or accuracy in pathway association.

Purpose of the Study:

  • To develop and implement a software tool for associating fuzzy clusters with biological pathways.
  • To validate the utility of this tool in a real-world biological problem.
  • To explore novel gene-pathway associations.

Main Methods:

  • Implementation of a correlation-based fuzzy c-means algorithm.
  • Integration with an enrichment test on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

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  • Application to gene expression data for lung cancer cell type classification.

    • Main Results:

    • The developed Java-based software tool successfully associated fuzzy clusters with biological pathways.
    • Satisfactory results were achieved in the classification of lung cancer cell types.
    • The workflow demonstrated potential for validating fuzzy clustering results.

    Conclusions:

    • The implemented workflow provides a valuable tool for bioinformatics research.
    • It facilitates the exploration of un-annotated associations between genes and biological pathways.
    • This approach can enhance the interpretation of gene expression data and aid in disease classification.