Protein-protein etkileşim tahmininde negatif örnekleme stratejilerinin karşılaştırmalı analizi

The accurate prediction of protein-protein interactions (PPIs) between host and pathogen proteins is essential for understanding viral infection mechanisms.However, a significant challenge is the lack of experimentally verified negative interactions (i.e., non-interacting pairs), which makes the development of effective negative sampling strategies essential. This thesis presents a comparative analysis of negative sampling strategies, specifically cluster-based methods. It proposes a novel Clustering-based Negative Sampling (CNS) method that aims to minimize the incorrect selection of negative interactions as positive interactions (i.e., experimentally confirmed interacting pairs). Furthermore, a structured evaluation methodology, Reliability of Sampling to Avoid False Examples (R-SAFE), is introduced to quantify the accuracy of these negative sampling methods. Experimental evaluations using four publicly available virus-host interaction datasets show that the characteristics of the datasets play a crucial role in the reliability of the negative sampling strategies. Therefore, a decision tree model is used to recommend the most appropriate negative sampling strategy based on the dataset features. The results emphasize the importance of negative sampling strategies to improve the predictive reliability of PPI models.