Investigating the minimum required number of genes for optimum classification of myopathy microarray data

Argiris Sakellariou, Despina Sanoudou, George Spyrou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The investigation of potential microarray markers, which in turn will speed up the molecular analysis and provide reliable results on the benefit of patient care is of significant importance. Feature selection techniques, which aim at minimizing the dimensionality of the microarray data by keeping the most significant genes according to their expression values is a necessary component towards this goal. In the current article, we present an investigation regarding the minimum required subsets of genes, which best classify myopathy data. For this purpose, we developed a tool that facilitates the users to easily access/use multiple feature selection methods and subsequently perform classification of data. For the current study, five feature selection methods on datasets from two different myopathies have been utilized. Our findings reveal subsets of very small number of genes, which can successfully classify gene expression datasets from different patients with skeletal myopathies. In addition, we observe that similar classification results may be obtained from completely different subsets of genes. The developed tool can expedite the identification of small gene subsets with high classification accuracy that could ultimately be used in the genetics clinics for diagnostic, prognostic and pharmacogenomic purposes.

Original languageEnglish
Title of host publicationFinal Program and Abstract Book - 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009
DOIs
Publication statusPublished - 1 Dec 2009
Externally publishedYes
Event9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009 - Larnaca, Cyprus
Duration: 4 Nov 20097 Nov 2009

Other

Other9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009
CountryCyprus
CityLarnaca
Period4/11/097/11/09

    Fingerprint

Keywords

  • Feature selection
  • Microarray data analysis
  • Molecular diagnosis
  • Myopathy

ASJC Scopus subject areas

  • Computer Science Applications
  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Sakellariou, A., Sanoudou, D., & Spyrou, G. (2009). Investigating the minimum required number of genes for optimum classification of myopathy microarray data. In Final Program and Abstract Book - 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009 [5394402] https://doi.org/10.1109/ITAB.2009.5394402