Uncertainty‐aware network alignment

2021 ◽  
Author(s):  
Fan Zhou ◽  
Ce Li ◽  
Zijing Wen ◽  
Ting Zhong ◽  
Goce Trajcevski ◽  
...  
Keyword(s):  
Network Alignment

Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

2018 ◽  
Vol 14 (1) ◽  
pp. 4-10
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Gene Ontology ◽  
Topological Structure ◽  
Metabolic Networks ◽  
Biological Network ◽  
Genomic Sequence ◽  
Network Alignment ◽  
Future Directions ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks

Background:Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.Objective and Method:Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.Results and Conclusion:We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

scholarly journals Data-driven biological network alignment that uses topological, sequence, and functional information

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shawn Gu ◽  
Tijana Milenković
Keyword(s):  
Prediction Accuracy ◽  
Biological Network ◽  
Network Alignment ◽  
Data Driven ◽  
Sequence Information ◽  
Functional Prediction ◽  
Research Knowledge ◽  
Topological Information ◽  
Functional Knowledge ◽  
Related Proteins

Abstract Background Network alignment (NA) can transfer functional knowledge between species’ conserved biological network regions. Traditional NA assumes that it is topological similarity (isomorphic-like matching) between network regions that corresponds to the regions’ functional relatedness. However, we recently found that functionally unrelated proteins are as topologically similar as functionally related proteins. So, we redefined NA as a data-driven method called TARA, which learns from network and protein functional data what kind of topological relatedness (rather than similarity) between proteins corresponds to their functional relatedness. TARA used topological information (within each network) but not sequence information (between proteins across networks). Yet, TARA yielded higher protein functional prediction accuracy than existing NA methods, even those that used both topological and sequence information. Results Here, we propose TARA++ that is also data-driven, like TARA and unlike other existing methods, but that uses across-network sequence information on top of within-network topological information, unlike TARA. To deal with the within-and-across-network analysis, we adapt social network embedding to the problem of biological NA. TARA++ outperforms protein functional prediction accuracy of existing methods. Conclusions As such, combining research knowledge from different domains is promising. Overall, improvements in protein functional prediction have biomedical implications, for example allowing researchers to better understand how cancer progresses or how humans age.

scholarly journals G-CREWE: Graph CompREssion With Embedding for Network Alignment

2020 ◽  
Author(s):  
Kyle K. Qin ◽  
Flora D. Salim ◽  
Yongli Ren ◽  
Wei Shao ◽  
Mark Heimann ◽  
...  
Keyword(s):  
Network Alignment ◽  
Graph Compression

scholarly journals Precoding-Based Network Alignment Using Transform Approach for Acyclic Networks With Delay

2014 ◽  
Vol 60 (10) ◽  
pp. 6276-6302 ◽  
Author(s):  
Teja Damodaram Bavirisetti ◽  
Abhinav Ganesan ◽  
Krishnan Prasad ◽  
B. Sundar Rajan
Keyword(s):  
Network Alignment

Network Alignment by Representation Learning on Structure and Attribute

2019 ◽  
pp. 698-711 ◽  
Author(s):  
Thanh Trung Huynh ◽  
Van Vinh Tong ◽  
Chi Thang Duong ◽  
Thang Huynh Quyet ◽  
Quoc Viet Hung Nguyen ◽  
...  
Keyword(s):  
Representation Learning ◽  
Network Alignment

scholarly journals Deep Adversarial Network Alignment

2021 ◽  
Author(s):  
Tyler Derr ◽  
Hamid Karimi ◽  
Xiaorui Liu ◽  
Jiejun Xu ◽  
Jiliang Tang
Keyword(s):  
Network Alignment ◽  
Adversarial Network
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