Using multivariate methods to infer knowledge from genomic data

Liliana López-Kleine, Nicolás Molano, Luis Ospina

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)285-300
Number of pages16
JournalInternational Journal of Bioinformatics Research and Applications
Volume9
Issue number3
DOIs
StatePublished - Jan 1 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Clinical Biochemistry
  • Health Information Management

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