Mahmood Amintoosi; Eisa Kohan-Baghkheirati
Abstract
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. ...
Read More
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.
