Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks
Joint Authors
Zhong, Cheng
Zhang, Jinxiong
Lin, Hai Xiang
Wang, Mian
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-08-21
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Identification of protein complex is very important for revealing the underlying mechanism of biological processes.
Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks.
Recently, researchers are considering the dynamics of protein-protein interactions.
Dynamic PPI networks are closer to reality in the cell system.
It is expected that more protein complexes can be accurately identified from dynamic PPI networks.
In this paper, we use the undulating degree above the base level of gene expression instead of the gene expression level to construct dynamic temporal PPI networks.
Further we convert dynamic temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs) and propose a novel method to accurately identify more protein complexes from the constructed TI-PINs.
Owing to preserving continuous interactions within temporal interval, the constructed TI-PINs contain more dynamical information for accurately identifying more protein complexes.
Our proposed identification method uses multisource biological data to judge whether the joint colocalization condition, the joint coexpression condition, and the expanding cluster condition are satisfied; this is to ensure that the identified protein complexes have the features of colocalization, coexpression, and functional homogeneity.
The experimental results on yeast data sets demonstrated that using the constructed TI-PINs can obtain better identification of protein complexes than five existing dynamic PPI networks, and our proposed identification method can find more protein complexes accurately than four other methods.
American Psychological Association (APA)
Zhang, Jinxiong& Zhong, Cheng& Lin, Hai Xiang& Wang, Mian. 2019. Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks. BioMed Research International،Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1124793
Modern Language Association (MLA)
Zhang, Jinxiong…[et al.]. Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks. BioMed Research International No. 2019 (2019), pp.1-17.
https://search.emarefa.net/detail/BIM-1124793
American Medical Association (AMA)
Zhang, Jinxiong& Zhong, Cheng& Lin, Hai Xiang& Wang, Mian. Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1124793
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1124793