Article ; Online: Link Prediction with Continuous-Time Classical and Quantum Walks.
2023 Volume 25, Issue 5
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 ... ...
| 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. |
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| Language | English |
| Publishing date | 2023-04-28 |
| Publishing country | Switzerland |
| Document type | Journal Article |
| ZDB-ID | 2014734-X |
| ISSN | 1099-4300 ; 1099-4300 |
| ISSN (online) | 1099-4300 |
| ISSN | 1099-4300 |
| DOI | 10.3390/e25050730 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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