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Interaction network

About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.


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Journal ArticleDOI
TL;DR: A global interaction network of miRNA–mRNA in lung cancer will contribute to refining miRNA target predictions and developing novel therapeutic candidates.
Abstract: MicroRNAs (miRNAs) are small RNA molecules, about 20-25 nucleotides in length. They repress or degrade messenger RNA (mRNA) translation, which are involved in human cancer. In this study based on paired miRNA and mRNA expression profiles of non-small cell lung cancer samples, we constructed and analyzed miRNA-mRNA interaction network via several bioinformatics softwares and platforms. This integrative network is comprised of 249 nodes for mRNA, 90 nodes for miRNA and 290 edges that show regulations between target genes and miRNAs. The three miR-1207-5p, miR-1228* and miR-939 are the most connected miRNA that regulated a large number of genes. ST8SIA2, MED1 and HDAC4, SPN, which are targeted by multiple miRNAs and located in the center of the network, are involved in both lung cancer and nervous system via functional annotation analysis. Such a global interaction network of miRNA-mRNA in lung cancer will contribute to refining miRNA target predictions and developing novel therapeutic candidates.

41 citations

Journal ArticleDOI
TL;DR: The effects of persistence in walking patterns on interaction networks us- ing computer simulations that are parameterized using observed behavior of harvester ants are investigated, and the influence of animal personalities on network structure and function depends on the environment in which the animals reside.
Abstract: The function of a network is affected by its structure. For example, the presence of highly interactive individuals, or hubs, influences the extent and rate of information spread across a network. In a network of interactions, the duration over which individual variation in interactions persists may affect how the network operates. Individuals may persist in their behavior over time and across situations, often referred to as personality. Colonies of social insects are an example of a biological system in which the structure of the coordinated networks of interacting workers may greatly influence information flow within the colony, and therefore its collective behavior. Here I investigate the effects of persistence in walking patterns on interaction networks us- ing computer simulations that are parameterized using observed behavior of harvester ants. I examine how the duration of persis- tence in spatial behavior influences network structure. Furthermore, I explore how spatial features of the environment affect the relationship between persistent behavior and network structure. I show that as persistence increases, the skewness of the weighted degree distribution of the interaction network increases. However, this relationship holds only when ants are confined in a space with boundaries, but not when physical barriers are absent. These findings suggest that the influence of animal personalities on network structure and function depends on the environment in which the animals reside (Current Zoology 61 (1): 98-106, 2015).

41 citations

Journal ArticleDOI
TL;DR: In this article, two techniques to develop inter-technology networks using patent data, a patent interaction network (PIN) and a patent citation network (PCN), are reviewed and compared.
Abstract: As the interactions between technologies increase during the innovation process, which is well described in the concept of fusion technology or a multi-technology industry, recent innovation research has given much attention to inter-technology networks. This study reviews two techniques to develop such networks using patent data, a patent interaction network (PIN) and a patent citation network (PCN). This review's purpose is to understand their features and to argue that the two techniques can be complementary. It also tries to explain how the techniques can be used individually and collectively to provide useful information for innovation strategy through a case study. The case study focuses on Korean innovation strategy and the relations between 17 new growth engines announced by the Korean government. The analysis of the results is expected to help understand the hidden dynamics of technology interactions during the innovation process and will ultimately support to develop innovation strategy.

41 citations

Proceedings ArticleDOI
01 Jan 2007
TL;DR: The use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms, and the complex finding algorithm performs very well on interaction networks modified in this way.
Abstract: Protein complexes are fundamental for understanding principles of cellular organizations. Accurate and fast protein complex prediction from the PPI networks of increasing sizes can serve as a guide for biological experiments to discover novel protein complexes. However, protein complex prediction from PPI networks is a hard problem, especially in situations where the PPI network is noisy. We know from previous work that proteins that do not interact, but share interaction partners (level-2 neighbors) often share biological functions. The strength of functional association can be estimated using a topological weight, FS-Weight. Here we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. All direct and indirect interactions are first weighted using topological weight (FS-Weight). Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied on this modified network. We also propose a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. In this paper, we show that 1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; 2) our complex finding algorithm performs very well on interaction networks modified in this way. Since no any other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.

40 citations

Journal ArticleDOI
01 Jun 2020
TL;DR: Analysis of the information exchange among the particles reveals that learning from low-degree particles in the middle period of evolution is crucial for the swarm to achieve the global optimum, offering a new framework to improve the performance of swarm optimization and demonstrate the applicability of network science in designing optimization algorithms.
Abstract: Particle swarm optimization (PSO) aims at finding the optimum point in a high-dimension solution space by simulating the swarming and flocking behaviors in nature. Recent empirical studies of reconstructing the hidden interaction networks in flocking birds and schooling fish found that individuals play different roles in group decision making. An outstanding question is whether the performance of PSO can be improved by incorporating these empirical findings. Here, we systematically explore the impact of the heterogeneity of interaction network and individual's learning strategies to find that the corresponding network-based algorithm, network-based heterogeneous particle swarm optimization (NHPSO), significantly outperforms other PSO based and non-PSO-based comparative algorithms on our experiments with 18 test functions. Our further analysis of the information exchange among the particles reveals that learning from low-degree particles in the middle period of evolution is crucial for the swarm to achieve the global optimum. These results offer a new framework to improve the performance of swarm optimization and demonstrate the applicability of network science in designing optimization algorithms. Finally, the universality of NHPSO is demonstrated on an emerging application, the unmanned aerial vehicle communication coverage.

40 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202337
202290
2021183
2020221
2019201
2018163