Every year‎, ‎extensive experimental analysis is conducted to evaluate the anti-cancer properties of plants‎. ‎Developing a well-ranked list of potential anti-cancer plants based on verified anti-cancer metabolites can significantly reduce the time and cost required for plant evaluation‎. This paper proposes a method for generating such a ranked list by analyzing biological graphs of plant-metabolite interactions‎. ‎In this approach‎, ‎graph nodes are ranked based on specific graph features‎. ‎However‎, ‎a challenge arises in selecting the most informative graph features that ensure the resulting ranked plant list is more relevant‎, ‎prioritizing plants with greater anti-cancer properties at the top‎. ‎To address this challenge‎, ‎we propose the use of the Average Precision metric commonly used in information retrieval and recommender systems‎, ‎to compare different ranked lists‎. By constructing a network that captures the similarities between plants based on their shared metabolites‎, ‎and ranking plants using different combinations of graph features‎, ‎we can identify the subset of features that yields a ranked list with a higher Average Precision score‎. ‎This subset of features can then be considered the most suitable for recommending anti-cancer plants‎. ‎ The proposed method can be used to select the best graph features for screening unverified plant lists for anti-cancer properties‎, ‎increasing the likelihood of identifying plants with higher scores in the list that possess anti-cancer properties

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