Local versus global biological network alignment

Meng, Lei; Striegel, Aaron; Milenković, Tijana

doi:10.1093/bioinformatics/btw348

Cited by 70 publications

(109 citation statements)

References 40 publications

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“…From biological point of view, we observed that AlignMCL, which is a local aligner, has the highest agreement with the GO annotations. This is consistent with the observation of Meng et al [59] reporting that local aligners generally outperform global aligners in their prediction power for GO annotations. On the other hand, four methods with the best topological quality, namely TAME, GHOST, L-GRAAL, and MAGNA++, had the lowest Fscore for predicting GO terms.…”

Section: Alignment Of Human Versus Yeast Interactomessupporting

confidence: 92%

Triangular Alignment (TAME). A Tensor-based Approach for Higher-order Network Alignment

Mohammadi

Gleich

Kolda

et al. 2015

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Abstract-Network alignment has extensive applications in comparative interactomics. Traditional approaches aim to simultaneously maximize the number of conserved edges and the underlying similarity of aligned entities. We propose a novel formulation of the network alignment problem that extends topological similarity to higher-order structures and provides a new objective function that maximizes the number of aligned substructures. This objective function corresponds to an integer programming problem, which is NP-hard. Consequently, we identify a closely related surrogate function whose maximization results in a tensor eigenvector problem. Based on this formulation, we present an algorithm called Triangular AlignMEnt (TAME), which attempts to maximize the number of aligned triangles across networks. Using a case study on the NAPAbench dataset, we show that triangular alignment is capable of producing mappings with high node correctness. We further evaluate our method by aligning yeast and human interactomes. Our results indicate that TAME outperforms the state-of-art alignment methods in terms of conserved triangles. In addition, we show that the number of conserved triangles is more significantly correlated, compared to the conserved edge, with node correctness and co-expression of edges. Our formulation and resulting algorithms can be easily extended to arbitrary motifs.

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Section: Alignment Of Human Versus Yeast Interactomessupporting

confidence: 92%

Triangular Alignment (TAME). A Tensor-based Approach for Higher-order Network Alignment

Mohammadi

Gleich

Kolda

et al. 2015

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“…The dominance of SANA becomes obvious if one creates a score which is the product of topology and functional similarity, since every other algorithm has its score reduced significantly by at least one of the two; this is depicted in Figure 4. It has been observed previously that there is a trade-off between the competing objectives of maximizing topological quality and maximizing sequence and/or functional similarityPatro and Kingsford (2012); ; Meng et al (2015); Clark and Kalita (2015). However, to our knowledge this is the first time that it has been shown that many algorithms are not capable of fully leveraging the trade-off to both ends of the spectrum.…”

Section: Alignments With Known Mapping Inmentioning

confidence: 80%

“…While SPINAL sometimes matches or slightly beats SANA in functional similarity, it does so at a high cost to the topological quality of its alignments ( Figure 3). As pointed out by Meng et al (2015), local alignments tend to perform better than global alignments in functional similarity score, and SANA is no exception to this rule. We hypothesize that this a consequence of most global aligners forcing a globally 1-to-1 mapping on the alignment, thus resulting in suboptimal placement of proteins that may legitimately claim a right to map to more than one location in the other network.…”

Section: Alignments With Known Mapping Inmentioning

confidence: 86%

SANA: simulated annealing far outperforms many other search algorithms for biological network alignment

Mamano

Hayes

2017

Bioinformatics

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Every alignment algorithm consists of two orthogonal components: an objective function M measuring the quality an alignment, and a search algorithm that explores the space of alignments looking for ones scoring well according to M . We introduce a new search algorithm called SANA (Simulated Annealing Network Aligner) and apply it to protein-protein interaction networks using S 3 as the the topological measure. Compared against 12 recent algorithms, SANA produces 5-10 times as many correct node mappings as the others when the correct answer is known. We expose an anti-correlation in many existing aligners between their ability to produce good topological vs. functional similarity scores, whereas SANA usually outscores other methods in both measures. If given the perfect objective function encoding the identity mapping, SANA quickly converges to the perfect solution while many other algorithms falter. We observe that when aligning networks with a known mapping and optimizing only S 3 , SANA creates alignments that are not perfect and yet whose S 3 scores match that of the perfect alignment. We call this phenomenon saturation of the topological score. Saturation implies that a measure's correlation with alignment correctness falters before the perfect alignment is reached. This, combined with SANA's ability to produce the perfect alignment if given the perfect objective function, suggests that better objective functions may lead to dramatically better alignments. We conclude that future work should focus on finding better objective functions, and offer SANA as the search algorithm of choice.

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“…NETAL [42] and MAGNA++ [43] are among the best existing global network alignment methods relying on topological alignment [44], and they have outperformed graph aligner [39] and its variants [40]. NETAL [42] constructs global alignments greedily, and can optionally take node similarity into account.…”

Section: Related Workmentioning

confidence: 99%

Learning Kinematic Structure Correspondences Using Multi-Order Similarities

Chang

Fischer

Petit

et al. 2018

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-In this paper, we present a novel framework for finding the kinematic structure correspondences between two articulated objects in videos via hypergraph matching. In contrast to appearance and graph alignment based matching methods, which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Thus our method allows matching the structure of objects which have similar topologies or motions, or a combination of the two. Our main contributions can be summarised as follows: (i) casting the kinematic structure correspondence problem into a hypergraph matching problem by incorporating multi-order similarities with normalising weights, (ii) introducing a structural topology similarity measure by aggregating topology constrained subgraph isomorphisms, (iii) measuring kinematic correlations between pairwise nodes, and (iv) proposing a combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on synthetic and real data, outperforming various other state of the art methods. Our method is not limited to a specific application nor sensor, and can be used as building block in applications such as action recognition, human motion retargeting to robots, and articulated object manipulation amongst others.

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Local versus global biological network alignment

Cited by 70 publications

References 40 publications

Triangular Alignment (TAME). A Tensor-based Approach for Higher-order Network Alignment

Triangular Alignment (TAME). A Tensor-based Approach for Higher-order Network Alignment

SANA: simulated annealing far outperforms many other search algorithms for biological network alignment

Learning Kinematic Structure Correspondences Using Multi-Order Similarities

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