Improved Algorithm for Learning of Gene Regulatory Network Connectivity from Time Series Data

Barker et al. presents the GeneNet algorithm designed to learn genetic regulatory network connectivity from time series data. The GeneNet algorithm is similar to work by Yu et al (2004), however, it takes a new approach by computing ratios of conditional probabilities and accumulating votes to determine influence between species. The approach taken by Yu et al uses Dynamic Bayesian Networks (DBN) and a cumulative distribution function (cdf) to determine a score for each species that may influence a gene. GeneNet approaches the problem differently by searching for differences between time points.

The pseudocode of the GeneNet algorithm is as follows:

GeneNet(Species S, Expts E, Influences I, Thresholds T, Levels L)
L:=DetermineLevels(S,E,L)
foreach c element of S:
Y:=CreateInfluenceVectorSet(c,S,E,I,T,L)
Y:=CombineInfluenceVectors(c,S,E,I,T,L,Y)
I(c):=CompeteInfluenceVectors(c,S,E,T,L,Y)
return I

Due to the lack of time series data available, synthetic data sets were generated for comparison. Empirical studies were performed which pitted GeneNet versus Yu's DBN algorithm on these synthetic datasets. GeneNet had significantly better precision, recall, and runtime for the majority of experiments.

(See the paper in Transactions on Computation Biology and Bioinformatics, No. 8, March 2007.)