Survey of biological network alignment: cross-species analysis of conserved systems

Maskey, Sawal; Cho, Young-Rae

doi:10.1109/bibm47256.2019.8983132

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…A distributed version of this algorithm [73] and then its parallel version was proposed in [74]. A parallel maximal weighted matching algorithm based on auctions was proposed in [75], and a recent study found the weighted matching of a bipartite graph by partitioning the adjacency matrix of the graph to processors with significant speedups [76], and a survey of network alignment methods is provided in [77].…”

Section: Review Of Network Alignment Algorithmsmentioning

confidence: 99%

Graph-Theoretical Analysis of Biological Networks: A Survey

Erciyes

2023

Computation

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Biological networks such as protein interaction networks, gene regulation networks, and metabolic pathways are examples of complex networks that are large graphs with small-world and scale-free properties. An analysis of these networks has a profound effect on our understanding the origins of life, health, and the disease states of organisms, and it allows for the diagnosis of diseases to aid in the search for remedial processes. In this review, we describe the main analysis methods of biological networks using graph theory, by first defining the main parameters, such as clustering coefficient, modularity, and centrality. We then survey fundamental graph clustering methods and algorithms, followed by the network motif search algorithms, with the aim of finding repeating subgraphs in a biological network graph. A frequently appearing subgraph usually conveys a basic function that is carried out by that small network, and discovering such a function provides an insight into the overall function of the organism. Lastly, we review network alignment algorithms that find similarities between two or more graphs representing biological networks. A conserved subgraph between the biological networks of organisms may mean a common ancestor, and finding such a relationship may help researchers to derive ancestral relationships and to predict the future evolution of organisms to enable the design of new drugs. We provide a review of the research studies in all of these methods, and conclude using the current challenging areas of biological network analysis, and by using graph theory and parallel processing for high performance analysis.

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Section: Review Of Network Alignment Algorithmsmentioning

confidence: 99%

Graph-Theoretical Analysis of Biological Networks: A Survey

Erciyes

2023

Computation

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“…Alignment results can be used in various areas such as predicting functions of unannotated proteins, revealing mechanisms of certain diseases and reproducing a rooted phylogenetic tree based on the discovered evolutionarily conserved pathways or protein complexes and detected functional orthologs across species [1]. Most Global Network Alignment algorithms rely upon the assumption that the functions of smaller networks map one-to-one to the functions of bigger networks homologously unlike most Local Network Alignment algorithms that focus on overlapping highly conserved subnetworks by allowing many-to-many node mappings [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Parallel Exchange of Randomized SubGraphs for Optimization of Network Alignment: PERSONA

Tuncay

Conrad

Erdur

2023

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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The aim of Network Alignment in Protein-Protein Interaction Networks is discovering functionally similar regions between compared organisms. One major compromise for solving a network alignment problem is the trade-off among multiple similarity objectives while applying an alignment strategy. An alignment may lose its biological relevance while favoring certain objectives upon others due to the actual relevance of unfavored objectives. One possible solution for solving this issue may be blending the stronger aspects of various alignment strategies until achieving mature solutions. This study proposes a parallel approach called PERSONA that allows aligners to share their partial solutions continuously while they progress. All these aligners pursue their particular heuristics as part of a particle swarm that searches for multi-objective solutions of the same alignment problem in a reactive actor environment. The actors use the stronger portion of a solution as a subgraph that they receive from leading or other actors and send their own stronger subgraphs back upon evaluation of those partial solutions. Moreover, the individual heuristics of each actor takes randomized parameter values at each cycle of parallel execution so that the problem search space can thoroughly be investigated. The results achieved with PERSONA are remarkably optimized and balanced for both topological and node similarity objectives.

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Graph-Theoretical Analysis of Biological Networks

Erciyes¹

2023

Preprint

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: Biological networks such as protein interaction networks, gene regulation networks and metabolic pathways are examples of complex networks which are large graphs with small-world and scale-free properties. Analysis of these networks has a profound effect on our understanding the origins of life, health and disease states of organisms, and diagnose diseases to aid the search for remedial processes. In this review, we describe main analysis methods of biological networks using graph theory by first defining main parameters such as clustering coefficient, modularity and centrality. We then survey fundamental graph clustering methods and algorithms followed by the network motif search algorithms with the aim of finding repeating subgraphs in a biological network graph. A frequently appearing subgraph usually conveys a basic function carried out by that small network and discovering such a function provides an insight to the overall function of the organism. Lastly, we review network alignment algorithms that achieve to find similarities between two or more graphs representing biological networks. A conserved subgraph between the biological networks of organisms may mean a common ancestor and finding such relationship may help researchers derive ancestral relationships and predict the future evolution of organisms to enable designing new drugs. We conclude by the current challenging areas of biological network analysis using graph theory and parallel processing for high performance analysis

show abstract

Survey of biological network alignment: cross-species analysis of conserved systems

Cited by 3 publications

References 43 publications

Graph-Theoretical Analysis of Biological Networks: A Survey

Graph-Theoretical Analysis of Biological Networks: A Survey

Parallel Exchange of Randomized SubGraphs for Optimization of Network Alignment: PERSONA

Graph-Theoretical Analysis of Biological Networks

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