2006
DOI: 10.1093/bioinformatics/btl140
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Improved scoring of functional groups from gene expression data by decorrelating GO graph structure

Abstract: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods.

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Cited by 1,972 publications
(1,537 citation statements)
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“…Significance of enrichment of GO groups with differentially expressed genes was calculated with Fisher's exact test and the elim algorithm. 31 For generating lists of significant GO groups the p-value cutoff 0.01 was used.…”
Section: Discussionmentioning
confidence: 99%
“…Significance of enrichment of GO groups with differentially expressed genes was calculated with Fisher's exact test and the elim algorithm. 31 For generating lists of significant GO groups the p-value cutoff 0.01 was used.…”
Section: Discussionmentioning
confidence: 99%
“…GO enrichment analysis was used the topGO software (Alexa et al. 2006) by the “elim” method with a minimum node size of 6.…”
Section: Methodsmentioning
confidence: 99%
“…We further applied the 'elimination algorithm' (Alexa et al 2006) to account for redundant GO terms. We considered all the proteins of each age transition (2450 and 2451 respectively), independent of any threshold and of the detection of the protein itself in the other age transition (Fig.…”
Section: Molecular Signature Of Oocytes During Maternal Ageingmentioning
confidence: 99%