GOMA: functional enrichment analysis tool based on GO modules / 癌症
Chinese Journal of Cancer
;
(12): 195-204, 2013.
Article
Dans Anglais
| WPRIM
| ID: wpr-295828
ABSTRACT
Analyzing the function of gene sets is a critical step in interpreting the results of high-throughput experiments in systems biology. A variety of enrichment analysis tools have been developed in recent years, but most output a long list of significantly enriched terms that are often redundant, making it difficult to extract the most meaningful functions. In this paper, we present GOMA, a novel enrichment analysis method based on the new concept of enriched functional Gene Ontology (GO) modules. With this method, we systematically revealed functional GO modules, i.e., groups of functionally similar GO terms, via an optimization model and then ranked them by enrichment scores. Our new method simplifies enrichment analysis results by reducing redundancy, thereby preventing inconsistent enrichment results among functionally similar terms and providing more biologically meaningful results.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Algorithmes
/
Tumeurs du sein
/
Biologie informatique
/
Séquençage par oligonucléotides en batterie
/
Analyse de profil d'expression de gènes
/
Bases de données génétiques
/
Réseaux de régulation génique
/
Gene Ontology
/
Génétique
/
Méthodes
Type d'étude:
Étude pronostique
Limites du sujet:
Femelle
/
Humains
langue:
Anglais
Texte intégral:
Chinese Journal of Cancer
Année:
2013
Type:
Article
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