Spectral similarity for analysis of DNA microarray time-series data
International Journal of Data Mining and Bioinformatics
Proceedings of the 2nd international workshop on Data and text mining in bioinformatics
GUEST EDITORIAL: Computational intelligence in solving bioinformatics problems
Artificial Intelligence in Medicine
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Negative correlations in collaboration: concepts and algorithms
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent pattern discovery without binarization: mining attribute profiles
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A new method to mine gene regulation relationship information
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Efficient matching and retrieval of gene expression time series data based on spectral information
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
Gene relation finding through mining microarray data and literature
Transactions on Computational Systems Biology V
BiMine+: An efficient algorithm for discovering relevant biclusters of DNA microarray data
Knowledge-Based Systems
Discretization in gene expression data analysis: a selected survey
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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Motivation: Analysis of gene expression data can provide insights into the positive and negative co-regulation of genes. However, existing methods such as association rule mining are computationally expensive and the quality and quantities of the rules are sensitive to the support and confidence values. In this paper, we introduce the concept of positive and negative co-regulated gene cluster (PNCGC) that more accurately reflects the co-regulation of genes, and propose an efficient algorithm to extract PNCGCs. Results: We experimented with the Yeast dataset and compared our resulting PNCGCs with the association rules generated by the Apriori mining algorithm. Our results show that our PNCGCs identify some missing co-regulations of association rules, and our algorithm greatly reduces the large number of rules involving uncorrelated genes generated by the Apriori scheme. Availability: The software is available upon request. Supplementary information: Supplementary tables and figures for this paper can be found at http://www.comp.nus.edu.sg/~jiliping/p1.html