Figure 4From: Inference of the Xenopus tropicalis embryonic regulatory network and spatial gene expression patterns Inference error versus number of random invalid edges. The proportional error (i.e., inference error) denotes the minimal cross-validation error divided by the minimal least-squares error of the linear regression without any regularization terms and averaged over five random networks. The blue, green and red curves represent 0, 5 and 20 random valid edges, respectively. 4 and 2 observations are considered in (A) and (B), respectively. When there are more valid edges (e.g., valid = 20), the errors are generally smaller as a whole. When only a few observations are available, the valid edges appear to be even more important. The cross-validation errors are in a large scale (i.e., 105) in (B), because they are divided by the least-squares errors, while fewer observations are easier to be over fitted with small least-squares errors (e.g., 10-6 ~ 10-4).Back to article page