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.

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Published
2025-01-01
DOI
https://doi.org/10.14428/esann/2025.es2025-153
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Alessio Micheli, Domenico Tortorella, Alessandro Dipalma
Bioinformatics and Genomic Networks
article

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

Alessio Micheli, Domenico Tortorella, Alessandro Dipalma
article en

Abstract

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.

University of Pisa (IT), Istituto Nazionale di Fisica Nucleare, Sezione di Pisa (IT)
European Commission
Openalex Percentile: Top 93%
Bioinformatics and Genomic Networks
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