A Novel Computational Approach for Global Alignment for Multiple Biological Networks

Djeddi, Warith Eddine; Yahia, Sadok Ben; Nguifo, Engelbert Mephu

doi:10.1109/tcbb.2018.2808529

Cited by 16 publications

(3 citation statements)

References 42 publications

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“…GEDEVO-M and BEAMS (Alkan and Erten, 2014) introduce the existence of search-based multiple network aligners. MAPPIN (Djeddi et al, 2018) utilizes shared memory parallelism to improve the runtimes of calculating the similarities and alignments between graphs.…”

Section: Graph Alignmentmentioning

confidence: 99%

A Survey of Graph Comparison Methods with Applications to Nondeterminism in High-Performance Computing

Bhowmick

Bell

Taufer

2023

The International Journal of High Performance Computing Applica

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The convergence of extremely high levels of hardware concurrency and the effective overlap of computation and communication in asynchronous executions has resulted in increasing nondeterminism in High-Performance Computing (HPC) applications. Nondeterminism can manifest at multiple levels: from low-level communication primitives to libraries to application-level functions. No matter its source, nondeterminism can drastically increase the cost of result reproducibility, debugging workflows, testing parallel programs, or ensuring fault-tolerance. Nondeterministic executions of HPC applications can be modeled as event graphs, and the applications’ nondeterministic behavior can be understood and, in some cases, mitigated using graph comparison algorithms. However, a connection between graph comparison algorithms and approaches to understanding nondeterminism in HPC still needs to be established. This survey article moves the first steps toward establishing a connection between graph comparison algorithms and nondeterminism in HPC with its three contributions: it provides a survey of different graph comparison algorithms and a timeline for each category’s significant works; it discusses how existing graph comparison methods do not fully support properties needed to understand nondeterministic patterns in HPC applications; and it presents the open challenges that should be addressed to leverage the power of graph comparisons for the study of nondeterminism in HPC applications.

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Section: Graph Alignmentmentioning

confidence: 99%

A Survey of Graph Comparison Methods with Applications to Nondeterminism in High-Performance Computing

Bhowmick

Bell

Taufer

2023

The International Journal of High Performance Computing Applica

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“…NetCoffee is a global one-to-one multi-network alignment algorithm that calculates the topological similarity of nodes in different networks using the T-Coffee method, generates candidate matching node pairs by a maximal weight matching algorithm. Also, based on NetCoffee, the improved algorithms NetCoffee2 [ 26 ] and MAPPIN [ 27 ] have been proposed. The MPGM algorithm is a penetration-based graph matching algorithm that uses a seed-expansion strategy to generate many-to-many network alignment.…”

Section: Introductionmentioning

confidence: 99%

SAMNA: accurate alignment of multiple biological networks based on simulated annealing

Chen,

Wang,

Huang

2023

Journal of Integrative Bioinformatics

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Proteins are important parts of the biological structures and encode a lot of biological information. Protein–protein interaction network alignment is a model for analyzing proteins that helps discover conserved functions between organisms and predict unknown functions. In particular, multi-network alignment aims at finding the mapping relationship among multiple network nodes, so as to transfer the knowledge across species. However, with the increasing complexity of PPI networks, how to perform network alignment more accurately and efficiently is a new challenge. This paper proposes a new global network alignment algorithm called Simulated Annealing Multiple Network Alignment (SAMNA), using both network topology and sequence homology information. To generate the alignment, SAMNA first generates cross-network candidate clusters by a clustering algorithm on a k-partite similarity graph constructed with sequence similarity information, and then selects candidate cluster nodes as alignment results and optimizes them using an improved simulated annealing algorithm. Finally, the SAMNA algorithm was experimented on synthetic and real-world network datasets, and the results showed that SAMNA outperformed the state-of-the-art algorithm in biological performance.

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“…In Chapter 3 of this dissertation, we use a reputable and generalpurpose algorithm to uncover which social signals best predict creativity at dyadic level. Similarly, Chapter 4 demonstrates an implementation of an algorithm developed for and implemented to date solely in the field of bio-informatics (namely, in research on aligning graphs determined by protein-to-protein interactions) (Djeddi, Yahia, & Nguifo, 2018); we apply the method to graphs described by the social signals collected with wearables to uncover similar pattern alignment which could not have been revealed with traditional computational or statistical methods.…”

Section: Machine Learning Algorithms -"New" Tools For Analyzing "New"mentioning

confidence: 99%

Sending all the right signals

Francu¹

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A Novel Computational Approach for Global Alignment for Multiple Biological Networks

Cited by 16 publications

References 42 publications

A Survey of Graph Comparison Methods with Applications to Nondeterminism in High-Performance Computing

A Survey of Graph Comparison Methods with Applications to Nondeterminism in High-Performance Computing

SAMNA: accurate alignment of multiple biological networks based on simulated annealing

Sending all the right signals

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