Finding differentially expressed genes for pattern generation

Bioinformatics. 2005 Feb 15;21(4):445-50. doi: 10.1093/bioinformatics/bti189. Epub 2004 Dec 17.

Abstract

Motivation: It is important to consider finding differentially expressed genes in a dataset of microarray experiments for pattern generation.

Results: We developed two methods which are mainly based on the q-values approach; the first is a direct extension of the q-values approach, while the second uses two approaches: q-values and maximum-likelihood. We present two algorithms for the second method, one for error minimization and the other for confidence bounding. Also, we show how the method called Patterns from Gene Expression (PaGE) (Grant et al., 2000) can benefit from q-values. Finally, we conducted some experiments to demonstrate the effectiveness of the proposed methods; experimental results on a selected dataset (BRCA1 vs BRCA2 tumor types) are provided.

Contact: alhajj@cpsc.ucalgary.ca.

Publication types

  • Comparative Study
  • Evaluation Study

MeSH terms

  • Algorithms*
  • Artificial Intelligence
  • BRCA2 Protein / genetics
  • BRCA2 Protein / metabolism*
  • Biomarkers, Tumor / genetics
  • Biomarkers, Tumor / metabolism*
  • Breast Neoplasms / diagnosis
  • Breast Neoplasms / genetics
  • Breast Neoplasms / metabolism*
  • Carrier Proteins / genetics
  • Carrier Proteins / metabolism*
  • Cluster Analysis
  • Computing Methodologies
  • Gene Expression Profiling / methods*
  • Humans
  • Models, Genetic
  • Models, Statistical
  • Oligonucleotide Array Sequence Analysis / methods*
  • Pattern Recognition, Automated / methods*
  • Ubiquitin-Protein Ligases

Substances

  • BRCA2 Protein
  • Biomarkers, Tumor
  • Carrier Proteins
  • BRAP protein, human
  • Ubiquitin-Protein Ligases