Learning genetic regulatory network connectivity from time series data

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Publication Type Journal Article
School or College College of Engineering
Department Electrical & Computer Engineering
Creator Myers, Chris J.
Other Author Barker, Nathan A.; Kuwahara, Hiroyuki
Title Learning genetic regulatory network connectivity from time series data
Date 2011
Description Abstract-Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This information can be used to understand the genetic regulatory network that generates the data. Typically, Bayesian analysis approaches are applied which neglect the time series nature of the experimental data, have difficulty in determining the direction of causality, and do not perform well on networks with tight feedback. To address these problems, this paper presents a method to learn genetic network connectivity which exploits the time series nature of experimental data to achieve better causal predictions. This method first breaks up the data into bins. Next, it determines an initial set of potential influence vectors for each gene based upon the probability of the gene's expression increasing in the next time step. These vectors are then combined to form new vectors with better scores. Finally, these influence vectors are competed against each other to determine the final influence vector for each gene. The result is a directed graph representation of the genetic network's repression and activation connections. Results are reported for several synthetic networks with tight feedback showing significant improvements in recall and runtime over Yu's dynamic Bayesian approach. Promising preliminary results are also reported for an analysis of experimental data for genes involved in the yeast cell cycle.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Volume 8
Issue 1
First Page 152
Last Page 165
Language eng
Bibliographic Citation Barker, N. A., Myers, C. J., & Kuwahara, H. (2011). Learning genetic regulatory network connectivity from time series data. IEEE Transactions on Computational Biology and Bioinformatics, 8(1), 152-65. January-Feberuary.
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Reference URL https://collections.lib.utah.edu/ark:/87278/s6hd8cs6