Learning Genetic Regulatory Network Connectivity from Time Series Data
Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This paper proposes an efficient method to generate the genetic regulatory network inferred from time series data. Our method first encodes the data into levels. Next, it determines the set of potential parents for each gene based upon the probability of the gene’s expression increasing. After a subset of potential parents are selected, it determines if any genes in this set may have a combined effect. Finally, the potential sets of parents are competed against each other to determine the final set of parents. The result is a directed graph representation of the genetic network’s repression and activation connections. Our results on synthetic data generated from models for several genetic networks with tight feedback are promising.
KeywordsTime Series Data Genetic Network Potential Parent Dynamic Bayesian Network System Biology Markup Language
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