Reconstruction of Gene Regulatory Networks usingMultiple Datasets
This addresses the challenge of integrating dispersed gene regulatory data for biologists, though it is incremental as it builds on existing multi-dataset methods.
The authors tackled the problem of reconstructing gene regulatory networks from sparse data by developing GENEREF, an algorithm that iteratively combines multiple datasets to improve predictions, showing it outperforms dynGENIE3 and matches iRafNet on DREAM benchmarks.
Motivation: Laboratory gene regulatory data for a species are sporadic. Despite the abundance of gene regulatory network algorithms that employ single data sets, few algorithms can combine the vast but disperse sources of data and extract the potential information. With a motivation to compensate for this shortage, we developed an algorithm called GENEREF that can accumulate information from multiple types of data sets in an iterative manner, with each iteration boosting the performance of the prediction results. Results: The algorithm is examined extensively on data extracted from the quintuple DREAM4 networks and DREAM5's Escherichia coli and Saccharomyces cerevisiae networks and sub-networks. Many single-dataset and multi-dataset algorithms were compared to test the performance of the algorithm. Results show that GENEREF surpasses non-ensemble state-of-the-art multi-perturbation algorithms on the selected networks and is competitive to present multiple-dataset algorithms. Specifically, it outperforms dynGENIE3 and is on par with iRafNet. Also, we argued that a scoring method solely based on the AUPR criterion would be more trustworthy than the traditional score. Availability: The Python implementation along with the data sets and results can be downloaded from github.com/msaremi/GENEREF