Scale-Space for Discrete Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete Scale Spaces via Heat Equation
SIBGRAPI '01 Proceedings of the 14th Brazilian Symposium on Computer Graphics and Image Processing
On Learning Gene Regulatory Networks Under the Boolean Network Model
Machine Learning
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Estimating gene networks from expression data and binding location data via boolean networks
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
Growing Seed Genes from Time Series Data and Thresholded Boolean Networks with Perturbation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Observability of Boolean networks: A graph-theoretic approach
Automatica (Journal of IFAC)
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Network inference algorithms can assist life scientists in unraveling gene-regulatory systems on a molecular level. In recent years, great attention has been drawn to the reconstruction of Boolean networks from time series. These need to be binarized, as such networks model genes as binary variables (either "expressed” or "not expressed”). Common binarization methods often cluster measurements or separate them according to statistical or information theoretic characteristics and may require many data points to determine a robust threshold. Yet, time series measurements frequently comprise only a small number of samples. To overcome this limitation, we propose a binarization that incorporates measurements at multiple resolutions. We introduce two such binarization approaches which determine thresholds based on limited numbers of samples and additionally provide a measure of threshold validity. Thus, network reconstruction and further analysis can be restricted to genes with meaningful thresholds. This reduces the complexity of network inference. The performance of our binarization algorithms was evaluated in network reconstruction experiments using artificial data as well as real-world yeast expression time series. The new approaches yield considerably improved correct network identification rates compared to other binarization techniques by effectively reducing the amount of candidate networks.