Year: 2023
Type: article
Abstract: Protein-protein interaction (PPI) networks consist of the physical and/or functional interactions between the proteins of an organism, and they form the basis for the field of network medicine. Since the biophysical and high-throughput methods used to form PPI networks are expensive, time-consuming, and often contain inaccuracies, the resulting networks are usually incomplete. In order to infer missing interactions in these networks, we propose a novel class of link prediction methods based on continuous-time classical and quantum walks. In the case of quantum walks, we examine the usage of both the network adjacency and Laplacian matrices for specifying the walk dynamics. We define a score function based on the corresponding transition probabilities and perform tests on six real-world PPI datasets. Our results show that continuous-time classical random walks and quantum walks using the network adjacency matrix can successfully predict missing protein-protein interactions, with performance rivalling the state-of-the-art.
Source: Entropy
DOI: 10.3390/e25050730
Authors: Mark Goldsmith, Harto Saarinen, Guillermo García-Pérez, Joonas Malmi, Matteo A. C. Rossi, Sabrina Maniscalco
Institutions: University of Turku, University of Helsinki, Aalto University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,224
Funder: Academy of Finland
OpenAlex ID: W4367298031