Poster: Gene regulatory network inference using time lagged context likelihood of relatedness

In our previous work, we have shown that time lags can be incorporated in information theory based metrics to further improve the efficiency of gene regulatory network inference. In particular, we have studied the mutual information metric where we found that mutual information saturates after a certain data size. We also proposed the time lagged mutual information metric and showed that the accuracy of inference algorithms using time lagged mutual information was better. Scalability of the proposed algorithm was an issue in our previous work. CLR is one of the popular algorithms which can infer very large networks. In this poster, we propose a time lagged version of the CLR algorithm.