Index-Based Network Aligner of Protein-Protein Interaction Networks

Elmsallati, Ahed; Msalati, Abdulghani; Kalita, Jugal

doi:10.1109/tcbb.2016.2613098

Cited by 18 publications

(13 citation statements)

References 32 publications

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“…The results of SAlign and its variant, , are compared with prominent existing aligners on BioGRID (three network pairs) and HINT (five network pairs) datasets. Existing prominent techniques include HubAlign [ 1 ], ModuleAlign [ 7 ], NETAL [ 8 ], PROPER [ 9 ], IBNAL [ 10 ] and Magna++ [ 15 ]. The performance of IsoRank [ 16 ], PISwap [ 17 ], GHOST [ 18 ], PINALOG [ 19 ], L-GRALL [ 20 ], Great [ 21 ] and SPINAL [ 22 ] have been shown to be lower than most of the above mentioned algorithms, so we did not include these algorithms in our analysis.…”

Section: Resultsmentioning

confidence: 99%

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SAlign–a structure aware method for global PPI network alignment

2020

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Background High throughput experiments have generated a significantly large amount of protein interaction data, which is being used to study protein networks. Studying complete protein networks can reveal more insight about healthy/disease states than studying proteins in isolation. Similarly, a comparative study of protein–protein interaction (PPI) networks of different species reveals important insights which may help in disease analysis and drug design. The study of PPI network alignment can also helps in understanding the different biological systems of different species. It can also be used in transfer of knowledge across different species. Different aligners have been introduced in the last decade but developing an accurate and scalable global alignment algorithm that can ensures the biological significance alignment is still challenging. Results This paper presents a novel global pairwise network alignment algorithm, SAlign, which uses topological and biological information in the alignment process. The proposed algorithm incorporates sequence and structural information for computing biological scores, whereas previous algorithms only use sequence information. The alignment based on the proposed technique shows that the combined effect of structure and sequence results in significantly better pairwise alignments. We have compared SAlign with state-of-art algorithms on the basis of semantic similarity of alignment and the number of aligned nodes on multiple PPI network pairs. The results of SAlign on the network pairs which have high percentage of proteins with available structure are 3–63% semantically better than all existing techniques. Furthermore, it also aligns 5–14% more nodes of these network pairs as compared to existing aligners. The results of SAlign on other PPI network pairs are comparable or better than all existing techniques. We also introduce $$\hbox {SAlign}^{\mathrm{mc}}$$ SAlign mc , a Monte Carlo based alignment algorithm, that produces multiple network alignments with similar semantic similarity. This helps the user to pick biologically meaningful alignments. Conclusion The proposed algorithm has the ability to find the alignments that are more biologically significant/relevant as compared to the alignments of existing aligners. Furthermore, the proposed method is able to generate alternate alignments that help in studying different genes/proteins of the specie.

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Section: Resultsmentioning

confidence: 99%

“…ModuleAlign develop a novel method for using topological information that is based on hierarchical clustering [ 7 ]. IBNAL develop a clique based index to measure the topology of the proteins [ 10 ]. NETAL and PROPER use local topological measures to calculate the topology [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

SAlign–a structure aware method for global PPI network alignment

2020

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“…

is a set of GO terms of protein u . Later, the normalized version of GOC was proposed by Elmsallati et al [17] . The NGOC is defined to be:

where n is the total number of aligned proteins.…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…IBNAL, proposed by Elmsallati et al [17] first divides the PPI network into two individual subsets and creates a Clique Degree Signature vector for each node in the subset of subordinate nodes to keep the number of cliques that the subordinate node connected. Then IBNAL indexes all subordinate nodes and cliques for accelerating the next step of alignment extraction.…”

Section: Methodsmentioning

confidence: 99%

A review of protein–protein interaction network alignment: From pathway comparison to global alignment

Liao

2020

Computational and Structural Biotechnology Journal

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Network alignment provides a comprehensive way to discover the similar parts between molecular systems of different species based on topological and biological similarity. With such a strong basis, one can do comparative studies at a systems level in the field of computational biology. In this survey paper, we focus on protein–protein interaction networks and review some representative algorithms for network alignment in the past two decades as well as the state-of-the-art aligners. We also introduce the most popular evaluation measures in the literature to benchmark the performance of these approaches. Finally, we address several future challenges and the possible ways to conquer the existing problems of biological network alignment.

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“…Each suite consists of three different datasets generated by different network synthesis models (i.e., DMC, DMR, and CG), where each dataset contains ten network families generated independently by a given synthesis model. Since the original release, NAPAbench has been widely used for evaluating the performance of various network alignment algorithms [3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%