Search

Identifying pharmaceutical technology opportunities can help guide drug innovation effectively and reduce the failure rate of pharmaceutical research and development (R&D) activities. Multiple factors are neatly associated with pharmaceutical R&D activities. Adverse drug reaction (ADR) is one of the main reasons for drug R&D failure. However, the current technology opportunities identification (TOI) approaches seldom try to identify pharmaceutical-specific technology opportunities from the perspective of ADR. Except that, pharmaceutical R&D activities are a complex process involving the interaction of disease, drug, and gene. Nevertheless, pharmaceutical TOI research often overlooks the relationships among various diseases, ADRs, drugs, and genes. In response, this article proposed a domain-specific multilayer network framework for pharmaceutical TOI from the standpoint of ADRs. Compared with previous research, there are three extensions. The first extension is identifying pharmaceutical TOI from the ADR's perspective. The second extension is identifying pharmaceutical technological opportunities based on multilayer networks. The last extension is integrating multi-source data in multilayer networks. Then, the proposed framework is applied to hyperuricemia drugs to prove their efficiency and effectiveness. This framework helps explain the complex relationships among diseases, ADRs, drugs, and genes. Furthermore, the analysis results provide interesting insight into pharmaceutical TOI and extend the multilayer network theory. • Adverse drug reaction is one of the main reasons for pharmaceutical technology R&D failure. • Pharmaceutical technology opportunity identification has innovation dilemmas. • Identifying pharmaceutical technology opportunities from the view of ADRs adopt multilayer networks can mitigate those dilemmas. • This article proposes a systematic and specific framework to develop that multilayer network. • Machine learning and multilayer networks can combine as effective analytical tools. [ABSTRACT FROM AUTHOR]

• We investigate interconnectedness of ASEAN-4, regional & global financial markets. • We study information spillovers using multilayer network among various assets classes. • Extreme risk & volatility spillovers may indicate early financial crises. • ASEAN-4 connectedness remains stable, and global markets show robust connectedness. • Findings improve understanding of global financial structure amid deglobalization. This study investigates the interconnectedness of ASEAN-4, regional, and global financial markets by analyzing risk spillover layers using multilayer information spillover network topology across instruments including bonds, forex, and stocks. The findings uncover intricate relationships between markets. Extreme risk and volatility spillover layers may indicate early financial crises, while mean spillovers exhibit hysteresis, peaking during or after crises. ASEAN-4 interconnectedness remains relatively stable, while global markets demonstrate robust interconnectedness with high overlap. The findings improve understanding of global financial structure amid deglobalization, holding policymaker and participant implications by revealing complex connections during financial crises. [ABSTRACT FROM AUTHOR]

Networks are useful mathematical representations of systems of interrelated entities. In cases where the entities can be related via different factors, the models can be extended to form networks of networks or multilayer networks. However, analyzing multilayer networks can get increasingly more expensive as the number of layers increase. We address the problem of efficiently finding communities in multilayer networks. Communities are groups of tightly connected entities that indicate that entities in the group are similar. Here we demonstrate that given certain easily verifiable structural conditions, which we term as self preserving communities, we can use fundamental Boolean operations to combine the communities obtained from each network layer to obtain the communities over the entire multilayer network. Our approach, when applied to real-world datasets such as traffic accidents, shows that we can reduce the time to find communities in multilayer networks by over 40%. Our proposed technique makes several important contributions to the nascent area of multilayer networks. We present an elegant and low-cost method to combine results from multiple layers, without recomputing the combined layers. Our method also makes it easier to add and process new information at individual layers. Together, our approach addresses both the variety aspect of big data by handling multiple data types as well as the volume aspect by enabling fast analysis of data from multiple networks. [ABSTRACT FROM AUTHOR]

Social networks are complex systems composed of interdependent organizations and people with diverse network structures. Understanding network dynamics, such as exchange commitment, requires a methodological toolkit that does not assume away complexity. In this study, we extend a technique for analyzing longitudinal, multilayer network data called network alignment. We introduce a novel metric – intersect proportions – for analyzing similarity between divergent graphs. We demonstrate the application of network alignment and intersect proportions to the context of investor commitment to startups and entrepreneurs. Using this technique, we are able to disentangle exchange commitment across complex networks. [ABSTRACT FROM AUTHOR]

We construct an inter-bank multilayer network that includes direct linkages through interbank transactions and indirect linkages through bond investment, and investigate the systemic risk in Chinese commercial banks in case of credit shocks, liquidity shocks and asset price shocks. According to our study, indirect linkages contribute more to systemic risk than direct linkages, and systemic risks from different types of shocks have different characteristics. We also find an intriguing non-linear effect in the aggregation of different linkages in the multiplex structure by comparing the total risk and the sum of systemic risk of all layers. This study suggests that the regulator should pay attention to the pervasiveness and dynamics of systemic risk, with a particular focus on risk contagion caused by indirect linkages among commercial banks. • Constructs a multilayer interbank network to analyze the interconnectedness and risk contagion of China's banking sector. • Presents the dynamics of banking business and systemic risk in recent years. • Undertakes the decomposition of systemic risk and explores the relative importance of different contagion channels. • The results benefit supervision of the regulatory authorities and investment decisions of investors. [ABSTRACT FROM AUTHOR]

Compared with the general complex network, the multilayer network is more suitable for the description of reality. It can be used as a tool of network pharmacology to analyze the mechanism of drug action from an overall perspective. Combined with random walk algorithm, it measures the importance of nodes from the entire network rather than a single layer. Here a four-layer network was constructed based on the data about the action process of prescriptions, consisting of ingredients, target proteins, metabolic pathways and diseases. The random walk algorithm was used to calculate the betweenness centrality of the protein layer nodes to get the rank of their importance. According to above method, we screened out the top 10% proteins that play a key role in treatment. Prescriptions Xiaochaihu Decoction was taken as example to prove our method. The selected proteins were measured with the ones that have been validated to be associated with the treated diseases. The results showed that its accuracy was no less than the topology-based method of single-layer network. The applicability of our method was proved by another prescription Yupingfeng Decoction. Our study demonstrated that multilayer network combined with random walk algorithm was an effective method for pre-screening vital target proteins related to prescriptions. [ABSTRACT FROM AUTHOR]