An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner

Hayes, Wayne B.

doi:10.1007/978-1-4939-9873-9_18

Cited by 6 publications

(3 citation statements)

References 54 publications

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“…The optimizer uses a cost function that consists of an equally weighted linear combination of edge and node similarity scores. We used EC, ICS, and S3 edge similarity scores along with a graphlet orbit degree-based node similarity score (see the references [13][14][15]21,22] for details on each score's definition). Finally, we used SANA [13], an off-the-shelf network aligner, and the aforementioned similarity scores for alignment optimization.…”

Section: Numerical Resultsmentioning

confidence: 99%

Extending Undirected Graph Techniques to Directed Graphs via Category Theory

Pardo-Guerra,

George,

Morar

et al. 2024

Mathematics

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We use Category Theory to construct a ‘bridge’ relating directed graphs with undirected graphs, such that the notion of direction is preserved. Specifically, we provide an isomorphism between the category of simple directed graphs and a category we call ‘prime graphs category’; this has as objects labeled undirected bipartite graphs (which we call prime graphs), and as morphisms undirected graph morphisms that preserve the labeling (which we call prime graph morphisms). This theoretical bridge allows us to extend undirected graph techniques to directed graphs by converting the directed graphs into prime graphs. To give a proof of concept, we show that our construction preserves topological features when applied to the problems of network alignment and spectral graph clustering.

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Section: Numerical Resultsmentioning

confidence: 99%

Extending Undirected Graph Techniques to Directed Graphs via Category Theory

Pardo-Guerra,

George,

Morar

et al. 2024

Mathematics

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“…The objective of the NA problem, then, is to find an optimal alignment with respect to a given quality measure. It is easy to see that the NA problem is N P -hard by reducing the well-known N P -complete subgraph isomorphism problem to the decision version of the NA problem [13,9,3].…”

Section: Problem Formulationmentioning

confidence: 99%

Negative learning Ant colony optimization for network alignment

Corominas

Blum

Blesa

2022

Proceedings of the Genetic and Evolutionary Computation Conference

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The Network Alignment problem is a hard Combinatorial Optimization problem with a wide range of applications, especially in computational biology. Given two (or more) networks, the goal is to find a mapping between their respective nodes that preserves the topological and functional structure of the networks. In this work we extend a novel ant colony optimization approach for network alignment by adding a recently proposed Negative Learning mechanism. In particular, information for Negative Learning is obtained by solving sub-instances of the tackled problem instances at each iteration by means of an Integer Linear Programming solver. The results show that the proposed algorithm not only outperforms the standard ant colony optimization approach but also current state-of-the-art methods from the literature.

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“…Our random walk through search space is generated using simulated annealing, which has a rich history of success in optimizing NP-complete problems [25][26][27][28][29][30][31][32][33][34][35][36][37] . Its randomness is key: each run of our Simulated Annealing Network Aligner, or SANA 38,39 , follows a different, randomized path towards an alignment that uncovers close to the maximum amount of common topology that can be discovered between two networks 40 . Since each path to a near-optimal alignment is different, each run of SANA produces a different alignment-but all alignments have nearly the same, close-to-optimal score.…”

Section: Introductionmentioning

confidence: 99%

SANA: Cross-Species Prediction of Gene Ontology GO Annotations via Topological Network Alignment

Wang,

Atkinson,

Hayes

2022

Preprint

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Topological network alignment aims to align two networks node-wise in order to maximize the observed common connection (edge) topology between them. The topological alignment of two Protein-Protein Interaction (PPI) networks should thus expose protein pairs with similar interaction partners allowing, for example, the prediction of common Gene Ontology (GO) terms. Unfortunately, no network alignment algorithm based on topology alone has been able to achieve this aim, though those that include sequence similarity have seen some success. We argue that this failure of topology alone is due to the sparsity and incompleteness of the PPI network data of almost all species, which provides the network topology with a small signal-to-noise ratio that is effectively swamped when sequence information is added to the mix. Here we show that the weak signal can be detected using multiple stochastic samples of "good" topological network alignments, which allows us to observe regions of the two networks that are robustly aligned across multiple samples. The resulting Network Alignment Frequency (NAF) strongly correlates with GO-based Resnik semantic similarity and enables the first successful cross-species predictions of GO terms based on topology-only network alignments. Our best predictions have an AUPR of about 0.4, which is competitive with state-of-the-art algorithms, even when there is no observable sequence similarity and no known homology relationship. While our results provide only a "proof of concept" on existing network data, we hypothesize that predicting GO terms from topology-only network alignments will become increasingly practical as the volume and quality of PPI network data increase.

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An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner

Cited by 6 publications

References 54 publications

Extending Undirected Graph Techniques to Directed Graphs via Category Theory

Extending Undirected Graph Techniques to Directed Graphs via Category Theory

Negative learning Ant colony optimization for network alignment

SANA: Cross-Species Prediction of Gene Ontology GO Annotations via Topological Network Alignment

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