Mining Regulatory Network Connections by Ranking Transcription Factor Target Genes Using Time Series Expression Data

Reverse engineering the gene regulatory network is challenging because the amount of available data is very limited compared to the complexity of the underlying network. We present a technique addressing this problem through focussing on a more limited problem: inferring direct targets of a transcription factor from short expression time series. The method is based on combining Gaussian process priors and ordinary differential equation models allowing inference on limited potentially unevenly sampled data. The method is implemented as an R/Bioconductor package, and it is demonstrated by ranking candidate targets of the p53 tumour suppressor.