Diffusion Alignment Coefficient (DAC): A Novel Similarity Metric for Protein-Protein Interaction Network
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee Computer Soc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Interaction 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.
Açıklama
Anahtar Kelimeler
Diffusion, alignment, protein function similarity, protein-protein interaction networks
Kaynak
Ieee-Acm Transactions on Computational Biology and Bioinformatics
WoS Q Değeri
Q1
Scopus Q Değeri
N/A
Cilt
20
Sayı
2
