The full text of this article:

Dynamic algorithm for inferring qualitative models of Gene Regulatory Networks
by Yun Zheng, Chee Keong Kwoh
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 1, No. 2, 2006
Abstract: We introduce a novel algorithm, DFL (Discrete Function Learning), for reconstructing qualitative models of Gene Regulatory Networks (GRNs) from gene expression data in this paper. We analyse its complexity of O(k · N · n²) on the average and its data requirements. The experiments of synthetic Boolean networks show that the DFL algorithm is more efficient than current algorithms without loss of prediction performances. The results of yeast cell cycle gene expression data show that the DFL algorithm can identify biologically significant models with reasonable accuracy, sensitivity and high precision with respect to the literature evidences.

is only available to individual subscribers or to users at subscribing institutions.

Pay per view: If you are not a Subscriber and you just want to read the full contents of this article, please click here to purchase online access to the full-text of this article. Please allow 3 days + mailing time. Current price for article is Thirty Euros (€30)

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB) journal, that have been redirected here, please check if you have a registered username/password subscription with Inderscience. If that is the case, please Login:

If you are not yet a Subscriber to International Journal of Data Mining and Bioinformatics (IJDMB) journal, you can subscribe by following a few simple and quick steps. A subscription will give you complete access to all articles in the current issue, as well as to all articles in the previous three years, where applicable. Click here to subscribe.

Should you experience further difficulties or have any enquiries, please email subs@inderscience.com