By Topic

Time series gene expression data clustering and pattern extraction in Arabidopsis thaliana phosphatase-encoding genes

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

6 Author(s)
Sobhe Bidari, P. ; Dept. of Biomed. Eng., K.N. Toosi Univ. of Technol., Tehran ; Manshaei, R. ; Lohrasebi, T. ; Feizi, A.
more authors

Clustering of genes using their expression data has been a major topic in recent years. A large amount of gene expression data even in time series are obtained by microarray technology. Finding gene clusters with similar functions and interconnecting genes by networks has an important role in mining biological gene functional analysis. In this paper, two phase functional clustering has been presented as a new approach in gene clustering. The proposed approach is based on finding functional patterns of time series gene expression data by fuzzy C-means (FCM) and K-means methods. The gene function similarities over a number of experimental conditions are extracted using Pearson correlation between expression patterns of genes. This leads to visualize genes interconnections.

Published in:

BioInformatics and BioEngineering, 2008. BIBE 2008. 8th IEEE International Conference on

Date of Conference:

8-10 Oct. 2008