Inference of genetic networks from time-series of gene expression levels using random forests

Abstract

Huynh-Thu and colleagues initially introduce the random forest into field of genetic network inference. Their method, GENIE3, has performed well on genetic network inference problems. However, GENIE3 was designed only for analyzing static expression data that were measured under steady-state conditions. In order to infer genetic networks from time-series of gene expression data, this study proposes a new method based on the random forest. The proposed method has an ability to analyze both static and time-series data. When inferring a genetic network only from steady-state gene expression data, however, the proposed method is equivalent to GENIE3. Therefore, the proposed method can be seen as an extension of GENIE3. Through numerical experiments, we showed that the proposed method outperformed the existing inference methods on all of the 5 artificial genetic network inference problems.