3.1. Motif-Directed NCA
In the original NCA work [17], the prior information about the connectivity matrix, i.e., A(I), is provided by high-throughput experiments. However, the high-throughput ChIP-on-chip data are not available for some common species, such as rodents and humans [25]. With respect to this fact, Wang et al. [25] proposed a motif-directed NCA (mNCA) algorithm, which incorporates the motif information to obtain the prior network structure information and to infer TRNs. Due to the fact that the regulation between TFs and genes occurs only after TFs bind to the DNA sequence motifs in the gene’s promoter region [25], the authors incorporate the motif information to recover the interaction between TFs and genes. Moreover, since the prior topology information, either from ChIP-on-chip data or motif analysis, comes from biological experiments, it may contain many false positives/negatives. Thus, a stability analysis is further proposed in [25] to extract stable TFAs from the NCA algorithm. Specifically, the authors of [25] intentionally perturb the connectivity information and use the Pearson correlation coefficient as a stability measurement to determine whether the estimated TFAs are stable or not. Experimental results on muscle regeneration microarray data demonstrate that mNCA is able to reveal important TFAs, as well as their connectivity strength to corresponding genes.