Topology estimation of gene regulatory networks with relative expression level variations

Gene regulatory network reconstruction is essential in understanding a biological system. A fundamental problem with the existing methods is that direct and indirect regulations can not be easily distinguished. To overcome this drawback, a relative expression level variation (RELV) based inference algorithm is suggested in this paper, which mainly consists of RELV magnitude estimation, normalization and modification. This method can in principle avoid the so-called cascade errors. Computation results with the Size 100 sub-challenges of both DREAM3 and DREAM4 show that, the suggested algorithm can significantly outperform not only the widely adopted Z-score based method, but also the best team of both DREAM3 and DREAM4. In addition, the high precision of the obtained most reliable predictions shows that the suggested algorithm may be very helpful in guiding experiment designs.