Finding differentially expressed genes for pattern generation.

Osman Abul; Reda Alhajj; Faruk Polat; Ken Barker

doi:10.1093/bioinformatics/bti189

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Finding differentially expressed genes for pattern generation.

Osman Abul·Reda Alhajj·Faruk Polat·Ken Barker

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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.

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Algorithms Artificial Intelligence BRCA2 Protein Biomarkers, Tumor Breast Neoplasms Carrier Proteins Cluster Analysis Computing Methodologies Gene Expression Profiling Humans Models, Genetic Models, Statistical Oligonucleotide Array Sequence Analysis Pattern Recognition, Automated Ubiquitin-Protein Ligases Comparative Study Evaluation Study Journal Article

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Osman Abul, Reda Alhajj, Faruk Polat, Ken Barker. 2004-12-17. Finding differentially expressed genes for pattern generation.. https://doi.org/10.1093/bioinformatics%2Fbti189

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