Using multivariate methods to infer knowledge from genomic data Academic Article

journal

  • International Journal of Bioinformatics Research and Applications

abstract

  • Since the introduction of genome sequencing techniques several methods for genomic data preprocessing and analysis have been published and applied to answer different biological questions. Rarely, multivariate methods have been used to extract knowledge about protein roles. Two of the most informative types of data are gene expression data (microarrays) and phylogenetic profiles indicating presence of genes in other organisms and therefore providing information about their co-evolution. Here we show that these two types of data, analyzed by means of Principal Component Analysis and Non Parametric Discriminant Analysis provide useful information about protein function and their participation in virulence processes.

publication date

  • 2013-1-1

edition

  • 9

keywords

  • Data Analysis
  • Discriminant Analysis
  • Discriminant analysis
  • Gene Expression
  • Gene expression
  • Genes
  • Genome
  • Microarrays
  • Principal Component Analysis
  • Principal component analysis
  • Proteins
  • Virulence

International Standard Serial Number (ISSN)

  • 1744-5485

number of pages

  • 16

start page

  • 285

end page

  • 300