The Local Edge Machine: inference of dynamic models of gene regulation

Table 1 LEM outperforms existing network inference algorithms on both in silico and biological data

Using in silico networks 1–2 (Fig. 2) and 3–5 (Fig. 3), as well as a yeast cell-cycle network (Fig. 4), we compared LEM performance to existing algorithms. AUC-ROC scores labeled (AUC) were used to compare the performance of LEM to Inferelator, Granger Causality, Hill-DBN and Jump3. Matthew’s correlation coefficient (MCC) was used to compare LEM to TD-ARACNE and BANJO, which are binary classifiers and do not output numerical scores for network edges. No biological prior information was used for this comparison. Using dynamics data from each network, LEM better approximates the underlying network model than the other algorithms. See Additional file 1: Section 5 for a complete explanation of AUC-ROC and MCC scoring
AUC area under the curve, LEM Local Edge Machine, MCC Matthew’s correlation coefficient, ROC receiver-operating characteristic

ISSN: 1474-760X