# TGS
Time-varying Gene regulatory networks with Shortlisted candidate regulators

# Description: 
Rapid advancement in high-throughput gene expression measurement technologies has resulted in genome- scale 
time series datasets. Uncovering the underlying temporal sequence of gene regulatory events in the form of time-varying 
Gene Regulatory Networks (GRNs) demands computationally fast, accurate and highly scalable algorithms. To provide a flexible 
framework in a significantly time-efficient manner, a novel algorithm, namely TGS, is proposed here. TGS is shown to consume 
only 29 minutes for a microarray dataset with 4028 genes. Moreover, it provides the flexibility and time-efficiency, without 
losing the accuracy. Nevertheless, TGS’s main memory requirement grows exponentially with the number of genes, which it 
tackles by restricting the maximum number of regulators for each gene. Relaxing this restriction remains an important 
challenge as the true number of regulators is not known a prior. 
