By Topic

A fuzzy logic approach to infer transcriptional regulatory network in saccharomyces cerevisiae using promoter site prediction and gene expression pattern recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Cheng-Long Chuang ; Dept. of Bio-Ind. Mechatron. Eng., Nat. Taiwan Univ., Taipei ; Chung-Ming Chen ; Shieh, G.S. ; Joe-Air Jiang

A fuzzy logic approach, called FuzzyTRN, to infer transcriptional regulatory networks (TRN) in Saccharomyces cerevisiae is proposed. FuzzyTRN predicts potential regulators and their target genes using sequences analysis on transcription factor binding sites (TFBS) of transcriptional factors (TF) and promoter region of target genes. Those potential regulators and target genes are used to form vertices in the TRN. Furthermore, multiple sets of microarray gene expression data (MGED) are used by FuzzyTRN to predict links in the TRN. FuzzyTRN predicts transcriptional interactions by recognizing expression patterns of genes. In this study, a number of confirmed genetic interactions are utilized to train FuzzyTRN. 112 indirect genetic interactions that were confirmed by quantitative real-time polymerase chain reaction (qRT-PCR) experiments, and 259 and 86 direct genetic interactions that were collected by TRANSFAC database and literature surveying, were used as training set in this work. A simulation that encompasses 170 TFs and 40 target genes has been conducted and checked against YEASTRACT database to evaluate the performance of the proposed algorithm.

Published in:

Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on

Date of Conference:

1-6 June 2008