Abstract
We are concerned with the problem of inferring genetic regulatory networks from a collection of temporal observations. This is often done via estimating a Dynamic Bayesian Network (DBN) from time series of gene expression data. However, when applying this algorithm to the limited quantities of experimental data that nowadays technologies can provide, its estimation is not robust. We introduce a weak learners’ methodology for this inference problem, study few methods to produce Weak Dynamic Bayesian Networks (WDBNs), and demonstrate its advantages on simulated gene expression data.
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Berkman, O., Intrator, N. (2007). Robust Inference in Bayesian Networks with Application to Gene Expression Temporal Data. In: Haindl, M., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2007. Lecture Notes in Computer Science, vol 4472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72523-7_48
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DOI: https://doi.org/10.1007/978-3-540-72523-7_48
Publisher Name: Springer, Berlin, Heidelberg
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