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Protein functional module identification method combining topological features and gene expression data

BACKGROUND: The study of protein complexes and protein functional modules has become an important method to further understand the mechanism and organization of life activities. The clustering algorithms used to analyze the information contained in protein-protein interaction network are effective w...

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Detalles Bibliográficos
Autores principales: Zhao, Zihao, Xu, Wenjun, Chen, Aiwen, Han, Yueyue, Xia, Shengrong, Xiang, ChuLei, Wang, Chao, Jiao, Jun, Wang, Hui, Yuan, Xiaohui, Gu, Lichuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8185953/
https://www.ncbi.nlm.nih.gov/pubmed/34103008
http://dx.doi.org/10.1186/s12864-021-07620-3
Descripción
Sumario:BACKGROUND: The study of protein complexes and protein functional modules has become an important method to further understand the mechanism and organization of life activities. The clustering algorithms used to analyze the information contained in protein-protein interaction network are effective ways to explore the characteristics of protein functional modules. RESULTS: This paper conducts an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. CONCLUSIONS: The algorithm can effectively remove the noise data reflected by calculating the topological structure characteristic values in the PPI network through the similarity of gene expression patterns, and also properly use the information hidden in the gene expression data. The experimental results show that the ECTG algorithm can detect protein functional modules better.