Fuzzy c-means clustering with prior biological knowledge

Luis Tari, Chitta Baral, Seungchan Kim

Research output: Contribution to journalArticlepeer-review

70 Scopus citations

Abstract

We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annotations as prior knowledge to guide the process of grouping functionally related genes. Unlike traditional clustering methods, our method is capable of assigning genes to multiple clusters, which is a more appropriate representation of the behavior of genes. Two datasets of yeast (Saccharomyces cerevisiae) expression profiles were applied to compare our method with other state-of-the-art clustering methods. Our experiments show that our method can produce far better biologically meaningful clusters even with the use of a small percentage of Gene Ontology annotations. In addition, our experiments further indicate that the utilization of prior knowledge in our method can predict gene functions effectively. The source code is freely available at http://sysbio.fulton.asu.edu/gofuzzy/.

Original languageEnglish (US)
Pages (from-to)74-81
Number of pages8
JournalJournal of Biomedical Informatics
Volume42
Issue number1
DOIs
StatePublished - Feb 2009

Keywords

  • Fuzzy c-means clustering
  • Gene Ontology
  • Gene expression data
  • Gene function prediction
  • Saccharomyces cerevisiae yeast
  • Semi-supervised clustering

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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