Research Article
Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information
Table 1
Performance comparisons of the nsiGGM with the JGGM and GGM using data simulated along with predefined module genes.
| Methods | # of noise genes | Sensitivity (s.e.) | Specificity (s.e.) | Youden (s.e.) |
| nsiGGM | 30 | 0.2217 (0.0253) | 0.9433 (0.0036) | 0.1650 (0.0257) | 40 | 0.2125 (0.0133) | 0.9472 (0.0053) | 0.1598 (0.0117) | 50 | 0.2034 (0.019) | 0.9481 (0.0035) | 0.1515 (0.0175) |
| JGGM | 30 | 0.2433 (0.04) | 0.8685 (0.0273) | 0.1118 (0.0161) | 40 | 0.2815 (0.0418) | 0.8321 (0.0309) | 0.1136 (0.0146) | 50 | 0.1920 (0.0425) | 0.8733 (0.0318) | 0.0653 (0.0124) |
| GGM | 30 | 0.2593 (0.0264) | 0.8325 (0.0214) | 0.0918 (0.0094) | 40 | 0.2752 (0.029) | 0.8050 (0.0257) | 0.0802 (0.0074) | 50 | 0.2177 (0.0303) | 0.8431 (0.0268) | 0.0608 (0.0085) |
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