Identifying gene regulatory networks from experimental data
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Clustering with Qualitative Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Correlation Clustering: maximizing agreements via semidefinite programming
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Aggregating inconsistent information: ranking and clustering
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
An explicit upper bound for the approximation ratio of the maximum gene regulatory network problem
CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology
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In this paper we apply a strategy to cluster gene expression data. In order to identify causal relationships among genes, we apply a pruning procedure [Chen et al., 1999] on the basis of the statistical cross-correlation function between couples of genes' time series. Finally we try to isolate genes' patterns in groups with positive causal relationships within groups and negative causal relation among groups. With this aim, we use a simple recursive clustering algorithm [Ailon et al., 2005].