Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to Psychiatric disorders

Inferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. In this paper, we propose two network inference methods based on a lasso-based random feature selection algorithm (LARF). There are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our method outperformed state of the art methods on simulated data, and LARF also was applied to the inference of gene regulatory networks associated with Psychiatric disorders.