Table 2 List of evaluation approaches.
From: A novel graph mining approach to predict and evaluate food-drug interactions
Title | Evaluation | Graph | Correct predictions | Methods |
|---|---|---|---|---|
Evaluation 1 | Remove random 30% of links from the DDIs (repeat 10 times) | Comprehensive evaluation for recovery of DDS similarity links | Match predicted links with the actual ones | All methods are applied |
Evaluation 2 | Remove random 30% of links (repeat 10 times) | Ground Truth using DrugBank | Match predicted links with DrugBank reported interactions | SP_2 (the best from evaluation 1 over disjoint graph) and RA (the best from evaluation 1 over joint graph) |
Evaluation 3 | Remove random 30% of links (repeat 10 times) | Whole graph including DDS, FDS, FFS | Match predicted links with the actual ones | SP_2 (the best from evaluation 1 over disjoint graph) and RA (the best from evaluation 1 over joint graph) |
Evaluation 4 | Prepare a list of gold standard food-drug interactions extracted from the literature. These interactions will be hidden from any training and will be used to measure and evaluate the validity of FDMine | Whole graph including DDS, FDS, FFS | Match predicted links with the actual food-drug interactions (gold standard dataset) | SP_2 (the best from evaluation 1 over disjoint graph) including SP_3 and RA (the best from evaluation 1 over joint graph) including AA, CN, and L3 |
