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 drug'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.
Network pharmacology of medicinal attributes and functions of
Chinese herbal medicines: (I) Basic statistics of medicinal attributes
and functions for more than 1100 Chinese herbal medicines
http://www.iaees.org/publications/journals/np/articles/2017-2(2)/basic-statistics-of-medicinal-attributes-and-functions.pdf
WenJun Zhang.Network Pharmacology,2017,2(2):17-37
Based on the Pharmacopoeia of the People's Republic of China, Chinese Materia Medica, and other resources, I collected a total of 1127 Chinese herbal medicines mainly with recorded chemical composition. Of which 210 families and approximately 2000 species of medicinal plants and fungi were involved. According to the comparison, in total of 69 medicinal attributes (Shu Xing) and 78 medicinal functions (Gon Xiao), including 22 medicinal organs or tissues, 7 taste attributes, 5 medicinal properties, 1 toxicity attribute, 22 chemical composition categories, 12 meridians and collaterals (Gui Jing), and 78 medicinal functions were determined. All of the Chinese herbal medicines were numerically coded according to drug name, species, family, 69 medicinal attributes and 78 medicinal functions. Finally, an interactive coding database, CHM-DATA, which contains 8 tables, was obtained. Statistics, e.g., totals, frequencies or probabilities, percentages, etc., were calculated on the basis of total population of medicines and families.
Network pharmacology of medicinal attributes and functions of
Chinese herbal medicines: (II) Relational networks and
pharmacological mechanisms of medicinal attributes and functions
of Chinese herbal medicines
http://www.iaees.org/publications/journals/np/articles/2017-2(2)/networks-and-mechanisms-of-medicinal-attributes-and-functions.pdf
WenJun Zhang.Network Pharmacology,2017,2(2):38-66
In present study, the database, CHM-DATA, with 1127 Chinese herbal medicines mainly having recorded chemical composition, involving 7 taste attributes, 5 medicinal properties, 1 toxicity attribute, 22 chemical composition categories, 12 meridians and collaterals (Gui Jing), and 78 medicinal functions (Gong Xiao), was used to calculate point correlations between these 125 attributes. Totally four relational networks, i.e., the networks for medicinal attributes and functions, for chemical composition and meridians and collaterals, for meridians and collaterals and medicinal functions, and for meridians and collaterals were constructed based on the significant point correlations. Network analysis indicated that the former three ones are scale-free complex networks and the last one tends to be a random network. Node degrees of the four networks follow power-law distribution. Detailed between-attribute relationships and medicinal mechanisms were revealed. For example, concerning chemical composition categories, alkaloids and amines have positive correlation / correspondence. More alkaloids correspond to more amines. Alkaloids negatively correlate with volatile oils / ordinary oils, carbohydrates / starch, ketones / flavonoids, and olefins. Alkaloids mainly function in decrease internal heat, dry dampness, etc. Organic acids and alkaloids have negative correlation. More organic acids mean the less alkaloids. Organic acids mainly act on large intestine meridians and collaterals, and function in moisten dryness. As for meridians and collaterals, kidney meridians and collaterals negatively correlate with lung meridians and collaterals, stomach meridians and collaterals, and large intestine meridians and collaterals. Kidney meridians and collaterals positively function in consolidate or warm kidney, invigorate male impotence (Yang) or strengthen male essence, strengthen bones and muscles, stop diarrheal, regulate menstruation or promote blood flow, relieve rheumatism or lubricate the joints, and negatively function in clear away heat, and detoxification.
Network pharmacology of medicinal attributes and functions of
Chinese herbal medicines: (III) Canonical correlation functions
between attribute classes and linear eignmodels of Chinese herbal medicines
http://www.iaees.org/publications/journals/np/articles/2017-2(3)/canonical-correlation-functions-between-attribute-classes.pdf
WenJun Zhang.Network Pharmacology,2017,2(3):67-81
In present study I used the data from CHM-DATA, the interactive database of 1127 Chinese herbal medicines. Canonical correlation functions were determined for taste attribute class (7 taste attributes), medicinal property class (5 medicinal properties), chemical composition class (22 chemical composition categories), meridians and colleterals class (12 meridians and colleterals), and medicinal function class (77 medicinal functions). Linear eignmodels were also developed for Chinese herbal medicines. Theoretically the attribute values of any Chinese herbal medicines meet the corresponding linear eignmodel. Matlab codes for canonical correlation analysis and linear eignmodel were given. Finally, the canonical correlation network for attribute classes of Chinese herbal medicines was constructed.
Network pharmacology of medicinal attributes and functions of
Chinese herbal medicines: (IV) Classification and network analysis of
medicinal functions of Chinese herbal medicines
http://www.iaees.org/publications/journals/np/articles/2017-2(3)/classification-and-network-analysis-of-medicinal-functions.pdf
WenJun Zhang.Network Pharmacology,2017,2(3):82-104
In present study I used the data from CHM-DATA, the interactive database of 1127 Chinese herbal medicines with 78 medicinal functions (attributes). The relational network for medicinal functions of Chinese herbal medicines was constructed using my earlier data and methods. Results of network analysis showed that the network is a scale-free complex network at the significance level of alpha=0.01. It was demonstrated that Clear away heat, Detoxification, Remove lung-heat or nourish lung, Promote secretion of saliva or body, Relieve pain, Regulate or enhance energy flow (Qi), Nourish or warm spleen / stomach / Qi, and Dispel endogenous wind, are the most influential medicinal functions. Seven main modules, corresponding links and module functions were identified and three of them were (Clear away heat, Detoxification, Relieve pain, Regulate or enhance energy flow (Qi)), (Loosen the bowels, Moisten dryness, Tonify blood, Nourish essential fluid (Yin), Inhibit or break energy flow (Qi)), and (Relieve external syndrome, Induce perspiration, Relieve muscular spasm, Expose exthanthema or promote eruption). PCA of 78 medicinal functions demonstrated that the medicinal functions 1-50 accounted for 79% of the total variance. There were not absolutely significant components and medicinal functions. The 78 components from PCA were substantially 78 independent and comprehensive medicinal functions. Major medicinal functions for every component can be simply determined by their importance and contribution coefficients in the component. New medicinal definition for some the most important principal components were given. Category characteristics of medicinal functions were described. At a certain level, for example, the medicinal functions -Consolidate or warm kidney, -Whet the appetite or reinforce stomach, Cool blood, -Regulate or enhance energy flow (Qi), -Nourish or warm spleen / stomach / Qi, Clear away heat, Detoxification, and Dispel endogenous cold, are the major attributes of the principal component F1. Of which the medicinal functions with the same sign (+ or -) have the same direction of medicinal action. Results of system cluster analysis demonstrated that (Consolidate or warm kidney, Invigorate male impotence (Yang) or strengthen male essence), (Relieve constipation, Loosen the bowels), and (Clear away heat, Detoxification) were classified into a class respectively at the earliest stage. They were the most similar medicinal functions from each other. Finally, some principles for incorporation and generation of medicinal functions were discussed and Matlab codes for PCA and system cluster analysis were given.