A multi-trait evaluation of network propagation for GWAS results

2019 ◽  
Author(s):  
Bence Bruncsics ◽  
Peter Antal
Keyword(s):  
Network Propagation

Network Propagation Reveals Novel Features Predicting Drug Response of Cancer Cell Lines

2016 ◽  
Vol 11 (2) ◽  
pp. 203-210 ◽  
Author(s):  
Jiguang Wang ◽  
Judith Kribelbauer ◽  
Raul Rabadan
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Lines ◽  
Network Propagation

scholarly journals Live Poultry Trading Drives China's H7N9 Viral Evolution and Geographical Network Propagation

2018 ◽  
Vol 6 ◽  
Author(s):  
Ruiyun Li ◽  
Tao Zhang ◽  
Yuqi Bai ◽  
Haochuan Li ◽  
Yong Wang ◽  
...  
Keyword(s):  
Viral Evolution ◽  
Network Propagation ◽  
Live Poultry

Community detection in social networks based on information propagation and user engagement

2021 ◽  
pp. 2150119
Author(s):  
Fuzhong Nian ◽  
Li Luo ◽  
Xuelong Yu ◽  
Xin Guo
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Detection Method ◽  
User Engagement ◽  
Connection Strength ◽  
Information Propagation ◽  
Edge Strength ◽  
Strength Based ◽  
Network Propagation ◽  
Overlapping Community

The iterative propagation of information between nodes will strengthen the connection strength between nodes, and the network can evolve into different groups according to difference edge strength. Based on this observation, we present the user engagement to quantify the influences of users different propagation modes to network propagation, and construct weight network to simulate real social network, and proposed the community detection method in social networks based on information propagation and user engagement. Our method can produce different scale communities and overlapping community. We also applied our method to real-world social networks. The experiment proved that the network spread and the community division interact with each other. The community structure is significantly different in the network propagation of different scales.

scholarly journals Network-based integrative analysis of lithium response in bipolar disorder using transcriptomic and GWAS data

2022 ◽  
Author(s):  
Vipavee Niemsiri ◽  
Sarah Brin Rosenthal ◽  
Caroline M. Nievergelt ◽  
Adam X. Maihofer ◽  
Maria C. Marchetto ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Focal Adhesion ◽  
Gene Networks ◽  
Gene List ◽  
Integrative Analysis ◽  
Functional Enrichment ◽  
Network Propagation ◽  
Lithium Response ◽  
The Difference ◽  
Effective Drugs

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (Phypergeometric=1.28E-09 and 4.10E-18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD.

scholarly journals Blockchain Network Propagation Mechanism Based on P4P Architecture

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Junjie Huang ◽  
Liang Tan ◽  
Sun Mao ◽  
Keping Yu
Keyword(s):  
Propagation Speed ◽  
Propagation Delay ◽  
Propagation Path ◽  
Propagation Mechanism ◽  
Internet Service ◽  
Network Provider ◽  
Network Propagation ◽  
System Overhead ◽  
Message Propagation

Blockchain is a mainstream technology in which many untrustworthy nodes work together to maintain a distributed ledger with advantages such as decentralization, traceability, and tamper-proof. The network layer communication mechanism in its architecture is the core of the networking method, message propagation, and data verification among blockchain nodes, which is the basis to ensure blockchain’s performance and key features. When blocks are propagated in peer-to-peer (P2P) networks with gossip protocol, the high propagation delay of the protocol itself reduces the propagation speed of the blocks, which is prone to the chain forking phenomenon and causes double payment attacks. To accelerate the propagation speed and reduce the fork probability, this paper proposes a blockchain network propagation mechanism based on proactive network provider participation for P2P (P4P) architecture. This mechanism first obtains the information of network topology and link status in a region based on the internet service provider (ISP), then it calculates the shortest path and link overhead of peer nodes using P4P technology, prioritizes the nodes with good local bandwidth conditions for transmission, realizes the optimization of node connections, improves the quality of service (QoS) and quality of experience (QoE) of blockchain networks, and enables blockchain nodes to exchange blocks and transactions through the secure propagation path. Simulation experiments show that the proposed propagation mechanism outperforms the original propagation mechanism of the blockchain network in terms of system overhead, rate of data success transmission, routing hops, and propagation delay.

scholarly journals Venn-diaNet : venn diagram based network propagation analysis framework for comparing multiple biological experiments

2019 ◽  
Vol 20 (S23) ◽  
Author(s):  
Benjamin Hur ◽  
Dongwon Kang ◽  
Sangseon Lee ◽  
Ji Hwan Moon ◽  
Gung Lee ◽  
...  
Keyword(s):  
Interaction Network ◽  
Venn Diagram ◽  
Analysis Framework ◽  
Seed Selection ◽  
Systematic Analysis ◽  
Main Research ◽  
Protein Protein Interaction ◽  
Propagation Analysis ◽  
Network Propagation ◽  
Main Research Topic

Abstract Background The main research topic in this paper is how to compare multiple biological experiments using transcriptome data, where each experiment is measured and designed to compare control and treated samples. Comparison of multiple biological experiments is usually performed in terms of the number of DEGs in an arbitrary combination of biological experiments. This process is usually facilitated with Venn diagram but there are several issues when Venn diagram is used to compare and analyze multiple experiments in terms of DEGs. First, current Venn diagram tools do not provide systematic analysis to prioritize genes. Because that current tools generally do not fully focus to prioritize genes, genes that are located in the segments in the Venn diagram (especially, intersection) is usually difficult to rank. Second, elucidating the phenotypic difference only with the lists of DEGs and expression values is challenging when the experimental designs have the combination of treatments. Experiment designs that aim to find the synergistic effect of the combination of treatments are very difficult to find without an informative system. Results We introduce Venn-diaNet, a Venn diagram based analysis framework that uses network propagation upon protein-protein interaction network to prioritizes genes from experiments that have multiple DEG lists. We suggest that the two issues can be effectively handled by ranking or prioritizing genes with segments of a Venn diagram. The user can easily compare multiple DEG lists with gene rankings, which is easy to understand and also can be coupled with additional analysis for their purposes. Our system provides a web-based interface to select seed genes in any of areas in a Venn diagram and then perform network propagation analysis to measure the influence of the selected seed genes in terms of ranked list of DEGs. Conclusions We suggest that our system can logically guide to select seed genes without additional prior knowledge that makes us free from the seed selection of network propagation issues. We showed that Venn-diaNet can reproduce the research findings reported in the original papers that have experiments that compare two, three and eight experiments. Venn-diaNet is freely available at: http://biohealth.snu.ac.kr/software/venndianet

scholarly journals Hydraulic Fracture Extending into Network in Shale: Reviewing Influence Factors and Their Mechanism

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Lan Ren ◽  
Jinzhou Zhao ◽  
Yongquan Hu
Keyword(s):  
Hydraulic Fracture ◽  
Influence Factors ◽  
Horizontal Stress ◽  
Practical Significance ◽  
Propagation Mechanism ◽  
Fluid Viscosity ◽  
Essential Difference ◽  
Research Results ◽  
Network Propagation ◽  
Shale Reservoir

Hydraulic fracture in shale reservoir presents complex network propagation, which has essential difference with traditional plane biwing fracture at forming mechanism. Based on the research results of experiments, field fracturing practice, theory analysis, and numerical simulation, the influence factors and their mechanism of hydraulic fracture extending into network in shale have been systematically analyzed and discussed. Research results show that the fracture propagation in shale reservoir is influenced by the geological and the engineering factors, which includes rock mineral composition, rock mechanical properties, horizontal stress field, natural fractures, treating net pressure, fracturing fluid viscosity, and fracturing scale. This study has important theoretical value and practical significance to understand fracture network propagation mechanism in shale reservoir and contributes to improving the science and efficiency of shale reservoir fracturing design.

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