Attributed Network Alignment: Problem Definitions and Fast Solutions

With the continuous development of biological experiment technology, more and more data related to uncertain biological networks needs to be analyzed. However, most of current alignment methods are designed for the deterministic biological network. Only a few can solve the probabilistic network alignment problem. However, these approaches only use the part of probabilistic data in the original networks allowing only one of the two networks to be probabilistic. To overcome the weakness of current approaches, an improved method called completely probabilistic biological network comparison alignment (C_PBNA) is proposed in this paper. This new method is designed for complete probabilistic biological network alignment based on probabilistic biological network alignment (PBNA) in order to take full advantage of the uncertain information of biological network. The degree of consistency (agreement) indicates that C_PBNA can find the results neglected by PBNA algorithm. Furthermore, the GO consistency (GOC) and global network alignment score (GNAS) have been selected as evaluation criteria, and all of them proved that C_PBNA can obtain more biologically significant results than those of PBNA algorithm.

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Community-Based Network Alignment for Large Attributed Network

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17 ◽

10.1145/3132847.3132904 ◽

2017 ◽

Cited By ~ 5

Author(s):

Zheng Chen ◽

Xinli Yu ◽

Bo Song ◽

Jianliang Gao ◽

Xiaohua Hu ◽

...

Keyword(s):

Network Alignment ◽

Community Based ◽

Attributed Network

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ELRUNA

Journal of Experimental Algorithmics ◽

10.1145/3450703 ◽

2021 ◽

Vol 26 ◽

pp. 1-32

Author(s):

Zirou Qiu ◽

Ruslan Shaydulin ◽

Xiaoyuan Liu ◽

Yuri Alexeev ◽

Christopher S. Henry ◽

...

Keyword(s):

Local Search ◽

Network Alignment ◽

Alignment Algorithm ◽

The Novel ◽

Alignment Problem ◽

Network Similarity ◽

Objective Value ◽

High Level ◽

Complex Phenomena ◽

Entire Network

Networks model a variety of complex phenomena across different domains. In many applications, one of the most essential tasks is to align two or more networks to infer the similarities between cross-network vertices and to discover potential node-level correspondence. In this article, we propose ELRUNA ( el imination ru le-based n etwork a lignment), a novel network alignment algorithm that relies exclusively on the underlying graph structure. Under the guidance of the elimination rules that we defined, ELRUNA computes the similarity between a pair of cross-network vertices iteratively by accumulating the similarities between their selected neighbors. The resulting cross-network similarity matrix is then used to infer a permutation matrix that encodes the final alignment of cross-network vertices. In addition to the novel alignment algorithm, we improve the performance of local search , a commonly used postprocessing step for solving the network alignment problem, by introducing a novel selection method RAWSEM ( ra ndom- w alk-based se lection m ethod) based on the propagation of vertices’ mismatching across the networks. The key idea is to pass on the initial levels of mismatching of vertices throughout the entire network in a random-walk fashion. Through extensive numerical experiments on real networks, we demonstrate that ELRUNA significantly outperforms the state-of-the-art alignment methods in terms of alignment accuracy under lower or comparable running time. Moreover, ELRUNA is robust to network perturbations such that it can maintain a close-to-optimal objective value under a high level of noise added to the original networks. Finally, the proposed RAWSEM can further improve the alignment quality with a smaller number of iterations compared with the naive local search method. Reproducibility : The source code and data are available at https://tinyurl.com/uwn35an.

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Improved Algorithm for the Network Alignment Problem with Application to Binary Diffing

Procedia Computer Science ◽

10.1016/j.procs.2021.08.099 ◽

2021 ◽

Vol 192 ◽

pp. 961-970

Author(s):

Elie Mengin ◽

Fabrice Rossi

Keyword(s):

Network Alignment ◽

Alignment Problem ◽

Improved Algorithm

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

Current Bioinformatics ◽

10.2174/1574893612666171020103747 ◽

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.

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Integrated Double-Sided Random Microlens Array Used for Laser Beam Homogenization

Micromachines ◽

10.3390/mi12060673 ◽

2021 ◽

Vol 12 (6) ◽

pp. 673

Author(s):

Wei Yuan ◽

Cheng Xu ◽

Li Xue ◽

Hui Pang ◽

Axiu Cao ◽

...

Keyword(s):

Laser Beam ◽

Microlens Array ◽

Energy Utilization ◽

Utilization Rate ◽

Periodic Lattice ◽

Lattice Distribution ◽

Speckle Field ◽

High Laser ◽

Alignment Problem ◽

In Series

Double microlens arrays (MLAs) in series can be used to divide and superpose laser beam so as to achieve a homogenized spot. However, for laser beam homogenization with high coherence, the periodic lattice distribution in the homogenized spot will be generated due to the periodicity of the traditional MLA, which greatly reduces the uniformity of the homogenized spot. To solve this problem, a monolithic and highly integrated double-sided random microlens array (D-rMLA) is proposed for the purpose of achieving laser beam homogenization. The periodicity of the MLA is disturbed by the closely arranged microlens structures with random apertures. And the random speckle field is achieved to improve the uniformity of the homogenized spot by the superposition of the divided sub-beams. In addition, the double-sided exposure technique is proposed to prepare the rMLA on both sides of the same substrate with high precision alignment to form an integrated D-rMLA structure, which avoids the strict alignment problem in the installation process of traditional discrete MLAs. Then the laser beam homogenization experiments have been carried out by using the prepared D-rMLA structure. The laser beam homogenized spots of different wavelengths have been tested, including the wavelengths of 650 nm (R), 532 nm (G), and 405 nm (B). The experimental results show that the uniformity of the RGB homogenized spots is about 91%, 89%, and 90%. And the energy utilization rate is about 89%, 87%, 86%, respectively. Hence, the prepared structure has high laser beam homogenization ability and energy utilization rate, which is suitable for wide wavelength regime.

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

BMC Bioinformatics ◽

10.1186/s12859-021-03971-6 ◽

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.

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