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 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.
Department of Computer Science and Information Systems, Southern Utah University, Cedar City, UT 84720, USA. barkern@suu.edu
Nathan A
NA
Barker
Chris J
CJ
Myers
Hiroyuki
H
Kuwahara
D000465:Algorithms; D001185:Artificial Intelligence; D010582:Bacteriophage lambda / Q000235:genetics; D001499:Bayes Theorem; D002453:Cell Cycle / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D005800:Genes, Fungal; D005814:Genes, Viral; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics; D013997:Time Factors