By Topic

Bayesian hierarchical model for estimating gene association network from microarray data

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

2 Author(s)
Dongxiao Zhu ; Bioinf. Program, Univ. of Michigan, Ann Arbor, MI ; Hero, A.O.

Estimating gene association networks from gene microar- ray data is the key to decipher complicated Web of functional relationship between genes. However, the process remains to be challenging due to the relatively few independent samples and the large amount of correlation parameters. In a gene association network, vertices represent genes, and edges represent biological association between genes. The network edges are declared to be present if the corresponding correlation parameters are significantly different from a non-zero threshold. The approach has been very useful in inferring gene association networks, and facilitating network based discovery. However, as a Frequentist approach, it often suffers from the "overfitting" problem especially for analyzing small sample size data. Approaches that are able to globally estimate the correlation parameters with variance regularization followed by the seamless correlation thresholding are highly desirable.

Published in:

Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on

Date of Conference:

28-30 May 2006