Network Pharmacology (ISSN 2415-1084)
http://www.iaees.org/publications/journals/np/np.asp
International Academy of Ecology and Environmental SciencesNetwork Pharmacology (ISSN 2415-1084); Publisher: International Academy of Ecology and Environmental Sciences;
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Network Pharmacology
http://www.iaees.org/publications/journals/np/online-version.asp
International Academy of Ecology and Environmental Sciences
Network pharmacology is an interdisciplinary science based on pharmacology, network biology, systems biology, bioinformatics, computational science, and other related scientific disciplines. In particular, it is a network-based science, just like other new proposed sciences (Zhang, 2016c). The NETWORK PHARMACOLOGY (ISSN 2415-1084) is an open access (BOAI definition), peer/open reviewed online journal (users are free to read, download, copy, distribute, print, search, or link to the full texts of the articles) that considers scientific articles in all different areas of network pharmacology. It devotes to understand the network interactions between a living organism and drugs that affect normal or abnormal biochemical function. It tries to exploit the pharmacological mechanism of drug action in the biological network, and helps to find drug targets and enhance the drugĄ¯s efficacy. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas.The scope of Network Pharmacology covers but not limits to: (1) theories, algorithms and software of network pharmacology; (2) theory, methods and case studies on dynamics, optimization and control of pharmacological networks (here generally refer to disease network, disease - disease, disease - drug, drug - drug, drug - target network, network targets - disease, and drug targets - disease network, etc.); (3) network analysis of disease/drug related pharmacological networks; (4) various pharmacological networks and interactions; (5) factors that affect drug metabolism, etc.
Network pharmacology: A further description
http://www.iaees.org/publications/journals/np/articles/2016-1(1)/network-pharmacology-a-further-description.pdf
WenJun Zhang.Network Pharmacology,2016,1(1):1-14
Network pharmacology devotes to understand the pharmacological mechanism of drug action in the network perspective. Based on previous studies, in present article I further outlined and defined the aims, scope, theory and methodology of network pharmacology.
Generate networks with power-law and exponential-law distributed
degrees: with applications in link prediction of tumor pathways
http://www.iaees.org/publications/journals/np/articles/2016-1(1)/generate-networks-with-power-law-distributed-degrees.pdf
WenJun Zhang, Xin Li.Network Pharmacology,2016,1(1):15-35
In present study I proposed a method for generating biological networks based on power-law (p(x)=x-lamda) and exponential-law (p(x)=e-lamda x) distribution functions. Given the parameter of power-law or exponential-law distribution function, lamda, the algorithm generates an expected frequency distribution according to the given parameter, thereafter creates an adjacency matrix in which (practical) frequency distribution of node degrees matches the expected frequency distribution. The results showed that power-law distribution function performs much better than exponential-law distribution function in generating networks. Using the revised algorithm, tumor related networks (pathways) are simulated and predicted. The results prove that the algorithm is overall effective in predicting network links (14.6-21.2 percent of correctly predicted links against 0.1-3.4 percent of that for random assignments). Matlab codes of the algorithms are given also.
A Matlab program for stepwise regression
http://www.iaees.org/publications/journals/np/articles/2016-1(1)/Matlab-program-for-stepwise-regression.pdf
Yanhong Qi, GuangHua Liu, WenJun Zhang.Network Pharmacology,2016,1(1):36-41
The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.
A Matlab program for finding shortest paths in the network:
Application in the tumor pathway
http://www.iaees.org/publications/journals/np/articles/2016-1(1)/Matlab-program-for-finding-shortest-paths.pdf
WenJun Zhang.Network Pharmacology,2016,1(1):42-53
The Floyd algorithm is used to find shortest paths in a graph or network. In present article I present full Matlab codes of the Floyd algorithm for using in the studies of network pharmacology. As an example, it is used to find shortest paths in a tumor pathway.
A method for identifying hierarchical sub-networks / modules and
weighting network links based on their similarity in sub-network /
module affiliation
http://www.iaees.org/publications/journals/np/articles/2016-1(2)/identifying-hierarchical-sub-networks-modules.pdf
WenJun Zhang.Network Pharmacology,2016,1(2):54-65
Some networks, including biological networks, consist of hierarchical sub-networks / modules. Based on my previous study, in present study a method for both identifying hierarchical sub-networks / modules and weighting network links is proposed. It is based on the cluster analysis in which between-node similarity in sets of adjacency nodes is used. Two matrices, linkWeightMat and linkClusterIDs, are achieved by using the algorithm. Two links with both the same weight in linkWeightMat and the same cluster ID in linkClusterIDs belong to the same sub-network / module. Two links with the same weight in linkWeightMat but different cluster IDs in linkClusterIDs belong to two sub-networks / modules at the same hirarchical level. However, a link with an unique cluster ID in linkClusterIDs does not belong to any sub-networks / modules. A sub-network / module of the greater weight is the more connected sub-network / modules. Matlab codes of the algorithm are presented.
Finding trees in the network: Some Matlab programs and application
in tumor pathways
http://www.iaees.org/publications/journals/np/articles/2016-1(2)/finding-trees-in-the-network.pdf
WenJun Zhang.Network Pharmacology,2016,1(2):66-73
Both DFS and Minty algorithms are used to find trees in a network (graph). In present article I present full Matlab codes of the two algorithms for using in the studies of network pharmacology. Trees are found in tumor pathways.
Some methods for sensitivity analysis of systems / networks
http://www.iaees.org/publications/journals/np/articles/2016-1(3)/methods-for-sensitivity-analysis-of-systems-and-networks.pdf
WenJun Zhang.Network Pharmacology,2016,1(3):74-81
A network may considerably change with certain nodes, links, flows, or parameters. To find the most important nodes, links, or other parameters to determine network structure or performance is of significant. Sensitivity analysis is originated from systems science. It explores the relationship between parametric change and systematic output, and is used to find important parameters in the system model. In principle, the sensitivity analysis used in systems science can also be extended to network analysis in which the model output means network output, network stability, network flow, network structure, or other indices, and model input means network nodes, network links, network parameters, etc. In present article, some methods for sensitivity analysis of systems / networks are described in detail.
How to find cut nodes and bridges in the network? A Matlab program
and application in tumor pathways
http://www.iaees.org/publications/journals/np/articles/2016-1(3)/find-cut-nodes-and-bridges-in-the-network.pdf
WenJun Zhang.Network Pharmacology,2016,1(3):82-85
A connected graph X is a block, if and only if for any three vertices u, v and w in X, there exists a path from u to w and the path does not contain v. In present article I present full Matlab codes of the algorithm for finding cut nodes and bridges in the network.
A mathematical model for dynamics of occurrence probability of
missing links in predicted missing link list
http://www.iaees.org/publications/journals/np/articles/2016-1(4)/model-for-dynamics-of-occurrence-probability-of-missing-links.pdf
WenJun Zhang.Network Pharmacology,2016,1(4):86-94
In most of the link prediction methods, all predicted missing links are ranked according to their scores. In the practical application of prediction results, starting from the first link that has the highest score in the ranking list, we verify each link one by one through experiments or other ways. Nevertheless, how to find an occurrence pattern of true missing links in the ranking list has seldomly reported. In present study, I proposed a mathematical model for relationship between cumulative number of predicted true missing links (y) and cumulative number of predicted missing links (x): y=K(1-e-rx/K), where K is the expected total number of true missing links, and r is the intrinsic (maximum) occurrence probability of true missing links. It can be used to predict the changes of occurrence probability of true missing links, assess the effectiveness of a prediction method, and help find the mechanism of link missing in the network. The model was validated by six prediction methods using the data of tumor pathways.
Comparative study of risk administration in centralized and
distributed software development atmosphere
http://www.iaees.org/publications/journals/np/articles/2016-1(4)/risk-administration-in-centralized-and-distributed-software-development.pdf
Riaz Shah, F. Bahadur, Sheraz Ahmed, Faiza Kanwal.Network Pharmacology,2016,1(4):95-103
Risk administration is used to increase the possibility of success of any future project by exploring its reservations. It will meet all the remedies to make the software development project successful by keeping in view all the future problems that may occur during the project process. It includes the identification of risk and their assessment in the project course and tries to make improvement to make project constructive. Risk administration goals are to overcome project task risks those are identified before starting of the project and during the implementation. This paper describes the phases in the risk administration process and provided methods to analysis and safety of administration. The paper focuses on a study risk administration in centralized and distributed software development projects. This study recognizes valuable, constant and free communication as the basics for victorious risk administration. Therefore, it registers all incoming information memorize much in the same pattern as the "black box" device during an aircraft flight. The description and evaluation tools are also included, may be used during the risk administration study in the software development atmosphere.
Metabolic pathway of non-alcoholic fatty liver disease: Network
properties and robustness
http://www.iaees.org/publications/journals/np/articles/2017-2(1)/metabolic-pathway-of-non-alcoholic-fatty-liver-disease.pdf
WenJun Zhang, YuTing Feng.Network Pharmacology,2017,2(1):1-12
Nonalcoholic fatty liver disease (NAFLD) is a systematic and complex disease involving various cytokines/metabolites. In present article, we use methodology of network biology to analyze network properties of NAFLD metabolic pathway. It is found that the metabolic pathway of NAFLD is not a typical complex network with power-law degree distribution, p(x)=x^(-4.4275), x not less than 5. There is only one connected component in the metabolic pathway. The calculated cut cytokines/metabolites of the metabolic pathway are SREBP-1c, ChREBP, ObR, AMPK, IRE1alpha, ROS, PERK, elF2alpha, ATF4, CHOP, Bim, CASP8, Bid, CxII, Lipogenic enzymes, XBP1, and FFAs. The most important cytokine/metabolite for possible network robustness is FFAs, seconded by TNF-alpha. It is concluded that FFAs is the most important cytokine/metabolite in the metabolic pathway, seconded by ROS. FFAs, LEP, ACDC, CYP2E1, and Glucose are the only cytokines/metabolites that affect others without influences from other cytokines/metabolites. Finally, the IDs matrix for identifying possible sub-networks/modules is given. However, jointly combining the results of connectedness analysis and sub-networks/modules identification, we hold that there are not significant sub-networks/modules in the pathway.
Finding the shortest tree in the network: A Matlab program and
application in tumor pathway
http://www.iaees.org/publications/journals/np/articles/2017-2(1)/finding-the-shortest-tree-in-the-network.pdf
WenJun Zhang.Network Pharmacology,2017,2(1):13-16
In present article, I present full Matlab codes of Kruskal algorithm for calculating the shortest tree and use it in tumor pathway.