scholarly journals Yin and Yang of disease genes and death genes between reciprocally scale-free biological networks

2013 ◽  
Vol 41 (20) ◽  
pp. 9209-9217 ◽  
Author(s):  
Hyun Wook Han ◽  
Jung Hun Ohn ◽  
Jisook Moon ◽  
Ju Han Kim
Keyword(s):  
Biological Networks ◽  
Disease Genes ◽  
Scale Free ◽  
Yin And Yang ◽  
Death Genes

scholarly journals Disease genes and intracellular protein networks

2003 ◽  
Vol 15 (3) ◽  
pp. 223-227 ◽  
Author(s):  
S. Bortoluzzi ◽  
C. Romualdi ◽  
A. Bisognin ◽  
G. A. Danieli
Keyword(s):  
Protein Network ◽  
Cdna Libraries ◽  
Protein Networks ◽  
Disease Genes ◽  
Intracellular Protein ◽  
Scale Free ◽  
Specific Distribution ◽  
Human Genes ◽  
Genome Level ◽  
Important Nodes

By a computational approach we reconstructed genomic transcriptional profiles of 19 different adult human tissues, based on information on activity of 27,924 genes obtained from unbiased UniGene cDNA libraries. In each considered tissue, a small number of genes resulted highly expressed or “tissue specific.” Distribution of gene expression levels in a tissue appears to follow a power law, thus suggesting a correspondence between transcriptional profile and “scale-free” topology of protein networks. The expression of 737 genes involved in Mendelian diseases was analyzed, compared with a large reference set of known human genes. Disease genes resulted significantly more expressed than expected. The possible correspondence of their products to important nodes of intracellular protein network is suggested. Auto-organization of the protein network, its stability in time in the differentiated state, and relationships with the degree of genetic variability at genome level are discussed.

Introduction

2021 ◽  
pp. 1-36
Author(s):  
Vadim Zverovich
Keyword(s):  
Graph Theory ◽  
Biological Networks ◽  
Real Life ◽  
Scale Free ◽  
Graph Theoretic ◽  
Life Problems ◽  
Scale Free Networks ◽  
Web Graph ◽  
The Web

This chapter gives a brief overview of selected applications of graph theory, many of which gave rise to the development of graph theory itself. A range of such applications extends from puzzles and games to serious scientific and real-life problems, thus illustrating the diversity of applications. The first section is devoted to the six earliest applications of graph theory. The next section introduces so-called scale-free networks, which include the web graph, social and biological networks. The last section describes a number of graph-theoretic algorithms, which can be used to tackle a number of interesting applications and problems of graph theory.

scholarly journals Combined Heuristic Attack Strategy on Complex Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Marek Šimon ◽  
Iveta Dirgová Luptáková ◽  
Ladislav Huraj ◽  
Marián Hosťovecký ◽  
Jiří Pospíchal
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Biological Networks ◽  
Case Scenario ◽  
Worst Case ◽  
Scale Free ◽  
Power Law Exponent ◽  
Spreading Disease ◽  
Heuristic Criterion ◽  
Attack Strategy

Usually, the existence of a complex network is considered an advantage feature and efforts are made to increase its robustness against an attack. However, there exist also harmful and/or malicious networks, from social ones like spreading hoax, corruption, phishing, extremist ideology, and terrorist support up to computer networks spreading computer viruses or DDoS attack software or even biological networks of carriers or transport centers spreading disease among the population. New attack strategy can be therefore used against malicious networks, as well as in a worst-case scenario test for robustness of a useful network. A common measure of robustness of networks is their disintegration level after removal of a fraction of nodes. This robustness can be calculated as a ratio of the number of nodes of the greatest remaining network component against the number of nodes in the original network. Our paper presents a combination of heuristics optimized for an attack on a complex network to achieve its greatest disintegration. Nodes are deleted sequentially based on a heuristic criterion. Efficiency of classical attack approaches is compared to the proposed approach on Barabási-Albert, scale-free with tunable power-law exponent, and Erdős-Rényi models of complex networks and on real-world networks. Our attack strategy results in a faster disintegration, which is counterbalanced by its slightly increased computational demands.

scholarly journals SYNCHRONIZABILITY AND SYNCHRONIZATION DYNAMICS OF WEIGHED AND UNWEIGHED SCALE FREE NETWORKS WITH DEGREE MIXING

2007 ◽  
Vol 17 (07) ◽  
pp. 2419-2434 ◽  
Author(s):  
FRANCESCO SORRENTINO ◽  
MARIO DI BERNARDO ◽  
FRANCO GAROFALO
Keyword(s):  
Power Law ◽  
Biological Networks ◽  
Optimization Strategy ◽  
Power Law Distribution ◽  
Scale Free ◽  
Degree Correlation ◽  
Topological Features ◽  
Low Degree ◽  
Correlation Properties ◽  
Law Degree

We study the synchronizability and the synchronization dynamics of networks of nonlinear oscillators. We investigate how the synchronization of the network is influenced by some of its topological features such as variations of the power law degree distribution exponent γ and the degree correlation coefficient r. Using an appropriate construction algorithm based on clustering the network vertices in p classes according to their degrees, we construct networks with an assigned power law distribution but changing degree correlation properties. We find that the network synchronizability improves when the network becomes disassortative, i.e. when nodes with low degree are more likely to be connected to nodes with higher degree. We consider the case of both weighed and unweighed networks. The analytical results reported in the paper are then confirmed by a set of numerical observations obtained on weighed and unweighed networks of nonlinear Rössler oscillators. Using a nonlinear optimization strategy we also show that negative degree correlation is an emerging property of networks when synchronizability is to be optimized. This suggests that negative degree correlation observed experimentally in a number of physical and biological networks might be motivated by their need to synchronize better.

scholarly journals Identifying Disease Related Genes by Network Representation and Convolutional Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Bolin Chen ◽  
Yourui Han ◽  
Xuequn Shang ◽  
Shenggui Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Biological Networks ◽  
Dimensional Space ◽  
Scale Free ◽  
Network Representation ◽  
Disease Related Genes ◽  
Representation Method ◽  
Low Dimensional

The identification of disease related genes plays essential roles in bioinformatics. To achieve this, many powerful machine learning methods have been proposed from various computational aspects, such as biological network analysis, classification, regression, deep learning, etc. Among them, deep learning based methods have gained big success in identifying disease related genes in terms of higher accuracy and efficiency. However, these methods rarely handle the following two issues very well, which are (1) the multifunctions of many genes; and (2) the scale-free property of biological networks. To overcome these, we propose a novel network representation method to transfer individual vertices together with their surrounding topological structures into image-like datasets. It takes each node-induced sub-network as a represented candidate, and adds its environmental characteristics to generate a low-dimensional space as its representation. This image-like datasets can be applied directly in a Convolutional Neural Network-based method for identifying cancer-related genes. The numerical experiments show that the proposed method can achieve the AUC value at 0.9256 in a single network and at 0.9452 in multiple networks, which outperforms many existing methods.

scholarly journals Identifying digenic disease genes using machine learning in the undiagnosed diseases network

2020 ◽  
Author(s):  
Souhrid Mukherjee ◽  
Joy D Cogan ◽  
John H Newman ◽  
John A Phillips ◽  
Rizwan Hamid ◽  
...  
Keyword(s):  
Biological Networks ◽  
Rapid Screening ◽  
Disease Genes ◽  
Individual Genome ◽  
Genetic Causes ◽  
Gene Pairs ◽  
Monogenic Diseases ◽  
Variant Gene ◽  
Undiagnosed Diseases ◽  
Undiagnosed Diseases Network

ABSTRACTRare diseases affect hundreds of millions of people worldwide, and diagnosing their genetic causes is challenging. The Undiagnosed Diseases Network (UDN) was formed in 2014 to identify and treat novel rare genetic diseases, and despite many successes, more than half of UDN patients remain undiagnosed. The central hypothesis of this work is that many unsolved rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating even just pairs of variants for potential to cause disease is currently infeasible. To address this challenge, we developed DiGePred, a random forest classifier for identifying candidate digenic disease gene pairs using features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier using DIDA, the largest available database of known digenic disease causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN patients. DiGePred achieved high precision and recall in cross-validation and on a held out test set (PR area under the curve >77%), and we further demonstrate its utility using novel digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings like the UDN. Finally, to facilitate the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work facilitates the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

scholarly journals A novel structure-based control method for analyzing nonlinear dynamics in biological networks

2018 ◽  
Author(s):  
Wei-Feng Guo ◽  
Shao-Wu Zhang ◽  
Tao Zeng ◽  
Yan Li ◽  
Jianxi Gao ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Nonlinear Control ◽  
Biological Networks ◽  
Control Method ◽  
Biological Systems ◽  
Network Control ◽  
Theoretical Research ◽  
Control Methods ◽  
Scale Free ◽  
Vertex Set

AbstractExploring complex biological systems requires adequate knowledge of the system’s underlying wiring diagram but not its specific functional forms. Thus, exploration actually requires the concepts and approaches delivered by structure-based network control, which investigates the controllability of complex networks through a minimum set of input nodes. Traditional structure-based control methods focus on the structure of complex systems with linear dynamics and may not match the meaning of control well in some biological systems. Here we took into consideration the nonlinear dynamics of some biological networks and formalized the nonlinear control problem of undirected dynamical networks (NCU). Then, we designed and implemented a novel and general graphic-theoretic algorithm (NCUA) from the perspective of the feedback vertex set to discover the possible minimum sets of the input nodes in controlling the network state. We applied our NCUA to both synthetic networks and real-world networks to investigate how the network parameters, such as the scaling exponent and the degree heterogeneity, affect the control characteristics of networks with nonlinear dynamics. The NCUA was applied to analyze the patient-specific molecular networks corresponding to patients across multiple datasets from The Cancer Genome Atlas (TCGA), which demonstrates the advantages of the nonlinear control method to characterize and quantify the patient-state change over the other state-of-the-art linear control methods. Thus, our model opens a new way to control the undesired transition of cancer states and provides a powerful tool for theoretical research on network control, especially in biological fields.Author summaryComplex biological systems usually have nonlinear dynamics, such as the biological gene (protein) interaction network and gene co-expression networks. However, most of the structure-based network control methods focus on the structure of complex systems with linear dynamics. Thus, the ultimate purpose to control biological networks is still too complicated to be directly solved by such network control methods. We currently lack a framework to control the biological networks with nonlinear and undirected dynamics theoretically and computationally. Here, we discuss the concept of the nonlinear control problem of undirected dynamical networks (NCU) and present the novel graphic-theoretic algorithm from the perspective of a feedback vertex set for identifying the possible sets with minimum input nodes in controlling the networks. The NCUA searches the minimum set of input nodes to drive the network from the undesired attractor to the desired attractor, which is different from conventional linear network control, such as that found in the Maximum Matching Sets (MMS) and Minimum Dominating Sets (MDS) algorithms. In this work, we evaluated the NCUA on multiple synthetic scale-free networks and real complex networks with nonlinear dynamics and found the novel control characteristics of the undirected scale-free networks. We used the NCUA to thoroughly investigate the sample-specific networks and their nonlinear controllability corresponding to cancer samples from TCGA which are enriched with known driver genes and known drug target as controls of pathologic phenotype transitions. We found that our NCUA control method has a better predicted performance for indicating and quantifying the patient biological system changes than that of the state-of-the-art linear control methods. Our approach provides a powerful tool for theoretical research on network control, especially in a range of biological fields.

Transcriptomics to Metabolomics

2018 ◽  
pp. 188-206 ◽  
Author(s):  
Ankush Bansal ◽  
Pulkit Anupam Srivastava
Keyword(s):  
Biological Networks ◽  
Metabolic Networks ◽  
Topological Analysis ◽  
In Silico Analysis ◽  
Small World ◽  
Recent Decade ◽  
Bipartite Networks ◽  
Metabolomics Data ◽  
Scale Free ◽  
Gene Regulatory

A lot of omics data is generated in a recent decade which flooded the internet with transcriptomic, genomics, proteomics and metabolomics data. A number of software, tools, and web-servers have developed to analyze the big data omics. This review integrates the various methods that have been employed over the years to interpret the gene regulatory and metabolic networks. It illustrates random networks, scale-free networks, small world network, bipartite networks and other topological analysis which fits in biological networks. Transcriptome to metabolome network is of interest because of key enzymes identification and regulatory hub genes prediction. It also provides an insight into the understanding of omics technologies, generation of data and impact of in-silico analysis on the scientific community.

Quantifying the connectivity of scale-free and biological networks

2004 ◽  
Vol 21 (1) ◽  
pp. 1-8 ◽  
Author(s):  
J.S. Shiner ◽  
Matt Davison
Keyword(s):  
Biological Networks ◽  
Scale Free
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