Infer Genetic/Transcriptional Regulatory Networks by Recognition of Microarray Gene Expression Patterns Using Adaptive Neuro-Fuzzy Inference Systems

Author(s):  
Cheng-Long Chuang ◽  
Chung-Ming Chen ◽  
Joe-Air Jiang
2007 ◽  
Vol 19 (02) ◽  
pp. 71-78 ◽  
Author(s):  
Cheng-Long Chuang ◽  
Chung-Ming Chen ◽  
Grace S. Shieh ◽  
Joe-Air Jiang

A neuro-fuzzy inference system that recognizes the expression patterns of genes in microarray gene expression (MGE) data, called GeneCFE-ANFIS, is proposed to infer gene interactions. In this study, three primary features are utilized to extract genes' expression patterns and used as inputs to the neuro-fuzzy inference system. The proposed algorithm learns expression patterns from the known genetic interactions, such as the interactions confirmed by qRT-PCR experiments or collected through text-mining technique by surveying previously published literatures, and then predicts other gene interactions according to the learned patterns. The proposed neuro-fuzzy inference system was applied to a public yeast MGE dataset. Two simulations were conducted and checked against 112 pairs of qRT-PCR confirmed gene interactions and 77 TFs (Transcriptional Factors) pairs collected from literature respectively to evaluate the performance of the proposed algorithm.


Author(s):  
Nawrah Khader ◽  
Virlana M Shchuka ◽  
Oksana Shynlova ◽  
Jennifer A Mitchell

Abstract The onset of labour is a culmination of a series of highly coordinated and preparatory physiological events that take place throughout the gestational period. In order to produce the associated contractions needed for fetal delivery, smooth muscle cells in the muscular layer of the uterus (i.e. myometrium) undergo a transition from quiescent to contractile phenotypes. Here, we present the current understanding of the roles transcription factors play in critical labour-associated gene expression changes as part of the molecular mechanistic basis for this transition. Consideration is given to both transcription factors that have been well-studied in a myometrial context, i.e. activator protein 1 (AP-1), progesterone receptors (PRs), estrogen receptors (ERs), and nuclear factor kappa B (NF-κB), as well as additional transcription factors whose gestational event-driving contributions have been demonstrated more recently. These transcription factors may form pregnancy- and labour- associated transcriptional regulatory networks in the myometrium to modulate the timing of labour onset. A more thorough understanding of the transcription factor-mediated, labour-promoting regulatory pathways holds promise for the development of new therapeutic treatments that can be used for the prevention of preterm labour in at-risk women.


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