By Topic

A Fuzzy Data Mining Technique for the Reconstruction of Gene Regulatory Networks from Time Series Expression 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)
Ma, P.C.H. ; Dept. of Comput., Hong Kong Polytech. Univ. ; Chan, K.C.C.

For one to infer the overall structures of gene regulatory networks (GRNs), it is important to identify, for each gene in a GRN, which other genes can affect its expression and how they can affect it. Many existing approaches to reconstructing GRNs are developed to generate hypotheses about the presence or absence of interactions between genes so that laboratory tests can be carried out afterwards for verification. Since they are not intended to be used to predict if a gene has any interactions with other genes from an unseen sample (i.e., expression data that is not in the original database), this makes statistical verification of the reliability of the discovered gene interactions difficult. To better infer the structures of GRNs, we propose an effective fuzzy data mining technique in this paper. By transforming quantitative expression values into linguistic terms, the proposed technique is able to mine noisy, high dimensional time series expression data for interesting fuzzy sequential associations between genes. It is not only able to determine if a gene is dependent on another but also able to determine if a gene is supposed to be activated or inhibited. In addition, it can predict how a gene in an unseen sample would be affected by other genes in it. For evaluation, the proposed technique has been tested using real expression data and experimental results show that the use of fuzzy logic-based technique in gene expression data analysis can be very effective

Published in:

Computational Intelligence and Bioinformatics and Computational Biology, 2006. CIBCB '06. 2006 IEEE Symposium on

Date of Conference:

28-29 Sept. 2006