scholarly journals Approach to scaling in axion string networks

2021 ◽  
Vol 103 (10) ◽  
Author(s):  
Mark Hindmarsh ◽  
Joanes Lizarraga ◽  
Asier Lopez-Eiguren ◽  
Jon Urrestilla
Keyword(s):  
String Networks

scholarly journals Numerical Simulations of String Networks in the Abelian-Higgs Model

1998 ◽  
Vol 80 (11) ◽  
pp. 2277-2280 ◽  
Author(s):  
Graham Vincent ◽  
Nuno D. Antunes ◽  
Mark Hindmarsh
Keyword(s):  
Numerical Simulations ◽  
Higgs Model ◽  
Abelian Higgs Model ◽  
String Networks

scholarly journals deepNF: Deep network fusion for protein function prediction

2017 ◽  
Author(s):  
Vladimir Gligorijević ◽  
Meet Barot ◽  
Richard Bonneau
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Predictive Performance ◽  
Substantial Improvement ◽  
Function Prediction ◽  
Interaction Networks ◽  
Highly Nonlinear ◽  
High Level ◽  
String Networks

AbstractThe prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provide a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that cannot capture complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity.AvailabilitydeepNF is freely available at: https://github.com/VGligorijevic/deepNF

scholarly journals Cosmological evolution of semilocal string networks

2019 ◽  
Vol 377 (2161) ◽  
pp. 20190004
Author(s):  
A. Achúcarro ◽  
A. Avgoustidis ◽  
A. López-Eiguren ◽  
C. J. A. P. Martins ◽  
J. Urrestilla
Keyword(s):  
Time Evolution ◽  
Topological Defect ◽  
Cosmic Strings ◽  
Thermodynamic Description ◽  
New Physics ◽  
Cosmological Evolution ◽  
Length Scale ◽  
Closed Loops ◽  
Global Monopoles ◽  
String Networks

Semilocal strings—a particular limit of electroweak strings—are an interesting example of a stable non-topological defect whose properties resemble those of their topological cousins, the Abrikosov–Nielsen–Olesen vortices. There is, however, one important difference: a network of semilocal strings will contain segments. These are ‘dumbbells’ whose ends behave almost like global monopoles that are strongly attracted to one another. While closed loops of string will eventually shrink and disappear, the segments can either shrink or grow, and a cosmological network of semilocal strings will reach a scaling regime. We discuss attempts to find a ‘thermodynamic’ description of the cosmological evolution and scaling of a network of semilocal strings, by analogy with well-known descriptions for cosmic strings and for monopoles. We propose a model for the time evolution of an overall length scale and typical velocity for the network as well as for its segments, and some supporting (preliminary) numerical evidence. This article is part of a discussion meeting issue ‘Topological avatars of new physics’.

scholarly journals Onset of rigidity for stretched string networks

2005 ◽  
Vol 72 (6) ◽  
pp. 990-996 ◽  
Author(s):  
G. W Delaney ◽  
D Weaire ◽  
S Hutzler
Keyword(s):  
String Networks

scholarly journals Evolution of semilocal string networks: Large-scale properties

2014 ◽  
Vol 89 (6) ◽  
Author(s):  
A. Achúcarro ◽  
A. Avgoustidis ◽  
A. M. M. Leite ◽  
A. Lopez-Eiguren ◽  
C. J. A. P. Martins ◽  
...  
Keyword(s):  
Large Scale ◽  
Scale Properties ◽  
String Networks

Patterns of the cosmic microwave background from evolving string networks

Nature ◽  
1988 ◽  
Vol 335 (6189) ◽  
pp. 410-414 ◽  
Author(s):  
François R. Bouchet ◽  
David P. Bennett ◽  
Albert Stebbins
Keyword(s):  
Cosmic Microwave Background ◽  
Microwave Background ◽  
String Networks

scholarly journals CMB birefringence from ultralight-axion string networks

2021 ◽  
Vol 2021 (05) ◽  
pp. 055
Author(s):  
Mudit Jain ◽  
Andrew J. Long ◽  
Mustafa A. Amin
Keyword(s):  
String Networks

scholarly journals Network properties of cancer prognostic genes and their gene sets in the human protein interactome

2019 ◽  
Author(s):  
Jifeng Zhang ◽  
Cheng Jiang ◽  
Zhicheng Ji ◽  
Chenrun Wang
Keyword(s):  
Essential Gene ◽  
Housekeeping Gene ◽  
Human Protein ◽  
Random Sets ◽  
Gene Set ◽  
Gene Sets ◽  
Interactome Network ◽  
Protein Interactome ◽  
Network Modules ◽  
String Networks

Abstract Background Identifying prognostic genes (PG) is crucial for estimating survival time and providing pinpoint treatments for patients with cancer. However, prognostic genes sets (PGS) reported in most existing research have low reproducibility and overlap ever between the same cancers or their subtypes. Their common characteristic as well as the molecular mechanism of action is still elusive. Methods Here, we obtained nine prognostic gene sets (including 1,439 prognostic genes) of different types of cancer from 23 high quality literatures, and systemically investigated eight network topological properties for PG and PGS compared with background and four other gene sets (cancer gene set CA, essential gene set ES, housekeeping gene set HK, and metastasis-angiogenesis gene set MA) based on the HPRD and String networks. Results The results showed that PG did not occupy key positions in the human protein interactome network, and were more similar to ES rather than CA. Also, PGS had significantly small intraset distance (IAD) and interset distance (IED) in comparison with random sets. Further, we also found that PGS tended to have be distributed within network modules rather than between modules, the functional intersection of the modules enriched with PGS was closely related to cancer. Conclusions Our research reveals the common properties of cancer PG and PGS in the human protein interactome network, and can help us understand and discover cancer prognostic biomarkers.

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