Biomolecular Network

2013 ◽  
pp. 95-104
Keyword(s):  
Biomolecular Network

scholarly journals Biomolecular Network-Based Synergistic Drug Combination Discovery

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangyi Li ◽  
Guangrong Qin ◽  
Qingmin Yang ◽  
Lanming Chen ◽  
Lu Xie
Keyword(s):  
Drug Combination ◽  
Complex Disease ◽  
Recent Decade ◽  
Drug Combinations ◽  
Network Medicine ◽  
Disease Therapy ◽  
Biomolecular Network ◽  
Disease Related Genes ◽  
Disease Specific ◽  
Synergistic Drug Combinations

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

A Web-Based Tool for Visualization of Biomolecular Network Comparison

2014 ◽  
Vol 556-562 ◽  
pp. 5482-5487
Author(s):  
Hui Ran Zhang ◽  
Xiao Long Shen ◽  
Jiang Xie ◽  
Dong Bo Dai
Keyword(s):  
Open Source ◽  
Undirected Graphs ◽  
Web Based ◽  
Network Comparison ◽  
Biomolecular Networks ◽  
Biomolecular Network ◽  
Visualization Tools ◽  
Similarities And Differences ◽  
Adaptive Visualization

Analyzing similarities and differences between biomolecular networks comparison through website intuitively could be a convenient and effective way for researchers. Although several network comparison visualization tools have been developed, none of them can be integrated into websites. In this paper, a web-based tool kit named dynamically adaptive Visualization of Biomolecular Network Comparison (Bio-NCV) is designed and developed. The proposed tool is based on Cytyoscape.js – a popular open-source library for analyzing and visualizing networks. Bio-NCV integrates arjor.js which including the Barnes-Hut algorithm and the Traer Physics library for processing in order to express the dynamically adaptive initialization. In addition, in order to maintain consistency, the counterparts in other networks will change while the nodes and edges in one of the compared networks change. Furthermore, Bio-NCV can deal with both directed and undirected graphs.

A Method for Querying Conserved Subnetwork in a Large-Scale Biomolecular Network

2010 ◽  
pp. 473-478
Author(s):  
Jiang Xie ◽  
Weibing Feng ◽  
Shihua Zhang ◽  
Songbei Li ◽  
Guoyong Mao ◽  
...  
Keyword(s):  
Large Scale ◽  
Biomolecular Network

scholarly journals Analysis of Topological Parameters of Complex Disease Genes Reveals the Importance of Location in a Biomolecular Network

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 143 ◽  
Author(s):  
Xiaohui Zhao ◽  
Zhi-Ping Liu
Keyword(s):  
Complex Disease ◽  
Significant Degree ◽  
Clustering Coefficient ◽  
Gene Location ◽  
Disease Genes ◽  
Complex Disorders ◽  
Topological Parameters ◽  
Topological Features ◽  
Disease Mechanisms ◽  
Biomolecular Network

Network biology and medicine provide unprecedented opportunities and challenges for deciphering disease mechanisms from integrative viewpoints. The disease genes and their products perform their dysfunctions via physical and biochemical interactions in the form of a molecular network. The topological parameters of these disease genes in the interactome are of prominent interest to the understanding of their functionality from a systematic perspective. In this work, we provide a systems biology analysis of the topological features of complex disease genes in an integrated biomolecular network. Firstly, we identify the characteristics of four network parameters in the ten most frequently studied disease genes and identify several specific patterns of their topologies. Then, we confirm our findings in the other disease genes of three complex disorders (i.e., Alzheimer’s disease, diabetes mellitus, and hepatocellular carcinoma). The results reveal that the disease genes tend to have a higher betweenness centrality, a smaller average shortest path length, and a smaller clustering coefficient when compared to normal genes, whereas they have no significant degree prominence. The features highlight the importance of gene location in the integrated functional linkages.

scholarly journals Learning Latent Variable Gaussian Graphical Model for Biomolecular Network with Low Sample Complexity

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Yanbo Wang ◽  
Quan Liu ◽  
Bo Yuan
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Graphical Model ◽  
Real Data ◽  
Sample Complexity ◽  
Gaussian Graphical Model ◽  
Alternating Direction ◽  
Biomolecular Network ◽  
Ill Posed ◽  
Cancer Networks

Learning a Gaussian graphical model with latent variables is ill posed when there is insufficient sample complexity, thus having to be appropriately regularized. A common choice is convexl1plus nuclear norm to regularize the searching process. However, the best estimator performance is not always achieved with these additive convex regularizations, especially when the sample complexity is low. In this paper, we consider a concave additive regularization which does not require the strong irrepresentable condition. We use concave regularization to correct the intrinsic estimation biases from Lasso and nuclear penalty as well. We establish the proximity operators for our concave regularizations, respectively, which induces sparsity and low rankness. In addition, we extend our method to also allow the decomposition of fused structure-sparsity plus low rankness, providing a powerful tool for models with temporal information. Specifically, we develop a nontrivial modified alternating direction method of multipliers with at least local convergence. Finally, we use both synthetic and real data to validate the excellence of our method. In the application of reconstructing two-stage cancer networks, “the Warburg effect” can be revealed directly.

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