DiffNet: automatic differential functional summarization of dE-MAP networks.
Methods
; 69(3): 247-56, 2014 Oct 01.
Article
em En
| MEDLINE
| ID: mdl-25009128
ABSTRACT
The study of genetic interaction networks that respond to changing conditions is an emerging research problem. Recently, Bandyopadhyay et al. (2010) proposed a technique to construct a differential network (dE-MAPnetwork) from two static gene interaction networks in order to map the interaction differences between them under environment or condition change (e.g., DNA-damaging agent). This differential network is then manually analyzed to conclude that DNA repair is differentially effected by the condition change. Unfortunately, manual construction of differential functional summary from a dE-MAP network that summarizes all pertinent functional responses is time-consuming, laborious and error-prone, impeding large-scale analysis on it. To this end, we propose DiffNet, a novel data-driven algorithm that leverages Gene Ontology (go) annotations to automatically summarize a dE-MAP network to obtain a high-level map of functional responses due to condition change. We tested DiffNet on the dynamic interaction networks following MMS treatment and demonstrated the superiority of our approach in generating differential functional summaries compared to state-of-the-art graph clustering methods. We studied the effects of parameters in DiffNet in controlling the quality of the summary. We also performed a case study that illustrates its utility.
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Texto completo:
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Leveduras
/
Mapeamento de Interação de Proteínas
/
Redes Reguladoras de Genes
Tipo de estudo:
Guideline
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article