Mining fuzzy association patterns in gene expression data for gene function prediction

Patrick C.H. Ma, Chun Chung Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

The development in DNA microarray technologies has made the simultaneous monitoring of the expression levels of thousands of genes under different experimental conditions possible. Due to the complexity of the underlying biological processes and also the expression data generated by DNA microarrays are typically noisy and have very high dimensionality, accurate functional prediction of genes using such data is still a very difficult task. In this paper, we propose a fuzzy data mining technique, which is based on a fuzzy logic approach, for gene function prediction. For performance evaluation, the proposed technique has been tested with a genome-wide expression data. Experimental results show that it can be effective and outperforms other existing classification algorithms. In the separated experiments, we also show that the proposed technique can be used with other existing clustering algorithms commonly used for gene function prediction and can improve their performances as well.
Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2008
Pages84-89
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2008
Event2008 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2008 - Philadelphia, PA, United States
Duration: 3 Nov 20085 Nov 2008

Conference

Conference2008 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2008
Country/TerritoryUnited States
CityPhiladelphia, PA
Period3/11/085/11/08

ASJC Scopus subject areas

  • Molecular Biology
  • Information Systems
  • Biomedical Engineering

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