Diffusion Alignment Coefficient (DAC): A Novel Similarity Metric for Protein-Protein Interaction Network

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dc.contributor.authorAltuntas, Volkan
dc.date.accessioned2026-02-12T21:05:41Z
dc.date.available2026-02-12T21:05:41Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractInteraction networks can be used to predict the functions of unknown proteins using known interactions and proteins with known functions. Many graph theory or diffusion-based methods have been proposed, using the assumption that the topological properties of a protein in a network are related to its biological function. Here we seek to improve function prediction by finding more similar neighbors with a new diffusion-based alignment technique to overcome the topological information loss of the node. In this study, we introduce the Diffusion Alignment Coefficient (DAC) algorithm, which combines diffusion, longest common subsequence, and longest common substring techniques to measure the similarity of two nodes in protein interaction networks. As a proof of concept, our experiments, conducted on a real PPI networks S.cerevisiae and Homo Sapiens, demonstrated that our method obtained better results than competitors for MIPS and MSigDB Collections hallmark gene set functional categories. This is the first study to develop a measure of node function similarity using alignment to consider the positions of nodes in protein-protein interaction networks. According to the experimental results, the use of spatial information belonging to the nodes in the network has a positive effect on the detection of more functionally similar neighboring nodes.
dc.identifier.doi10.1109/TCBB.2022.3185406
dc.identifier.endpage903
dc.identifier.issn1545-5963
dc.identifier.issn1557-9964
dc.identifier.issue2
dc.identifier.pmid35737632
dc.identifier.scopus2-s2.0-85133736802
dc.identifier.scopusqualityN/A
dc.identifier.startpage894
dc.identifier.urihttps://doi.org/10.1109/TCBB.2022.3185406
dc.identifier.urihttps://hdl.handle.net/20.500.12885/7079
dc.identifier.volume20
dc.identifier.wosWOS:000965674700009
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherIeee Computer Soc
dc.relation.ispartofIeee-Acm Transactions on Computational Biology and Bioinformatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260212
dc.subjectDiffusion
dc.subjectalignment
dc.subjectprotein function similarity
dc.subjectprotein-protein interaction networks
dc.titleDiffusion Alignment Coefficient (DAC): A Novel Similarity Metric for Protein-Protein Interaction Network
dc.typeArticle

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