Robustness in Protein-Protein Interaction Networks: A Link Prediction Approach

Protein-protein interaction networks (PPINs) are indispensable in exploring complex biological systems, facilitating advancements in fields like drug discovery, protein function annotation, and disease mechanism elucidation. So far, predicting the dynamical properties of biochemical pathways has relied on costly numerical simulations. In this paper, we propose exploiting the topological information in PPINs to restate the problem of predicting pathway robustness as a link prediction task. Our experiments show that the PPIN topology can supply information on inter-pathway relationships, significantly improving predictions of the graph-agnostic baseline relying only on protein sequence embeddings.

Recommended citation: A. Dipalma, D. Tortorella, A. Micheli (2025). "Robustness in Protein-Protein Interaction Networks: A Link Prediction Approach." Proceedings of the 33rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2025), pp. 277-282.
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