SlopeMiner: An Improved Method for Mining Subtle Signals in Time Course Microarray Data
FAW '08 Proceedings of the 2nd annual international workshop on Frontiers in Algorithmics
Polynomial-time controllability analysis of boolean networks
ACC'09 Proceedings of the 2009 conference on American Control Conference
Genetic Networks and Soft Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Polynomial-time algorithm for controllability test of a class of Boolean biological networks
EURASIP Journal on Bioinformatics and Systems Biology
Pattern recognition in biological time series
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
eXploratory K-Means: A new simple and efficient algorithm for gene clustering
Applied Soft Computing
Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inference of Biological S-System Using the Separable Estimation Method and the Genetic Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 3.84 |
Motivation: Methods available for the inference of genetic regulatory networks strive to produce a single network, usually by optimizing some quantity to fit the experimental observations. In this article we investigate the possibility that multiple networks can be inferred, all resulting in similar dynamics. This idea is motivated by theoretical work which suggests that biological networks are robust and adaptable to change, and that the overall behavior of a genetic regulatory network might be captured in terms of dynamical basins of attraction. Results: We have developed and implemented a method for inferring genetic regulatory networks for time series microarray data. Our method first clusters and discretizes the gene expression data using k-means and support vector regression. We then enumerate Boolean activation–inhibition networks to match the discretized data. Finally, the dynamics of the Boolean networks are examined. We have tested our method on two immunology microarray datasets: an IL-2-stimulated T cell response dataset and a LPS-stimulated macrophage response dataset. In both cases, we discovered that many networks matched the data, and that most of these networks had similar dynamics. Contact: jfaulon@sandia.gov Supplementary information: Supplementary data are available at Bioinformatics online.