Table 4 Quantitative characteristics of DT models and ILP theories
From: Integrative relational machine-learning for understanding drug side-effect profiles
DT (# nodes per model) | ILP (# rules per theory) | |||
|---|---|---|---|---|
Avg (min-max) | % total | Avg (min-max) | % total | |
Model coverage (%) | 58 (32-67) | - | 83 (77-88) | - |
Model size | 11 (6-15) | - | 33 (16-40) | - |
Drug descriptors | ||||
Categories | 4 (1-7) | 34 | 6 (2-13) | 19 |
Targets | 3 (0-5) | 26 | 30 (23-39) | 90 |
Clusters | 4 (1-9) | 40 | 9 (4-14) | 27 |
Target descriptors | ||||
GO terms | NA | NA | 24 (16-31) | 73 |
Domains | NA | NA | 1 (0-2) | 1 |
Interactions | NA | NA | 8 (2-16) | 24 |
Pathways | NA | NA | 4 (1-8) | 12 |
GO relationships | NA | NA | 6 (3-9) | 19 |