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Background: Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms. Purpose: We systematically explore the capabilities of network-based approaches on natural products, using Panax ginseng as a case study. P. ginseng is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood. Methods: Our study compiled and constructed a network focusing on P. ginseng by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model. Results: We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms. Conclusion: our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research.

Entrepreneurial ecosystems are wealthy environments in which entrepreneurs, firms, and governments can operate frictionless, contributing to innovation and economic growth. The investigation of the structure of such systems is an open issue. We provide insights on this aspect through the formulation of seven network-based principles associating specific network metrics to distinct structural features of entrepreneurial ecosystems. In this way, we aim to support the measurement of the structural characteristics of an entrepreneurial ecosystem and the design of policy interventions in case of unmet properties. The proposed methodology is applied to an original network built on the relationships occurring on Twitter among 612 noteworthy start-ups from seven different European countries. This is a novel way to conceptualize entrepreneurial ecosystems considering online interactions. Thus, this work represents a first attempt to analyze the structure of entrepreneurial ecosystems considering their network architecture to guide policy-making decisions. Our results suggest a partial ecosystem-like nature of the analyzed network, providing evidence about possible policy recommendations. Plain English Summary: Entrepreneurial ecosystems can be considered complex systems characterized by many actors frequently interacting among them in a non-linear way. We propose a network-based approach to analyze such interactions, and to measure the structural characteristics of an entrepreneurial ecosystem. Specifically, we provide seven network-based principles that may help policy-makers monitor the structure of a given entrepreneurial ecosystem and design policy interventions to fulfill eventual missing properties. In this way, we fill a relevant gap in the literature that is the investigation of the structure of entrepreneurial ecosystems. We apply the methodology to an online network of European start-ups, showing the usefulness and flexibility of our approach, as well as the main implications of the obtained results. [ABSTRACT FROM AUTHOR]

Abstract ‘De novo’ drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called ‘in silico’ drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging. We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes. Indeed, further clinical analysis is needed to confirm the therapeutic effects of identified drugs on each breast cancer subtype.

'De novo' drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called 'in silico' drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging. We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes. Indeed, further clinical analysis is needed to confirm the therapeutic effects of identified drugs on each breast cancer subtype. [ABSTRACT FROM AUTHOR]

Acute leukemia results from a series of mutational events that alter cell growth and proliferation. Mutations result in protein changes that orchestrate growth alterations characteristic of leukemia. Proteomics is a methodology appropriate for study of protein changes found in leukemia. The high-throughput reverse phase protein array (RPPA) technology is particularly well-suited for the assessment of protein changes in samples derived from clinical trials. This review discusses the technical, methodological, and analytical issues related to the successful development of acute leukemia RPPAs. To obtain representative protein sample lysates, samples should be prepared from freshly collected blood or bone marrow material. Variables such as sample shipment, transit time, and holding temperature only have minimal effects on protein expression. CellSave preservation tubes are preferred for cells collected after exposure to chemotherapy, and incorporation of standardized guidelines for antibody validation is recommended. A more systematic biological approach to analyze protein expression is desired, searching for recurrent patterns of protein expression that allow classification of patients into risk groups, or groups of patients that may be treated similarly. Comparing RPPA protein analysis between cell lines and primary samples shows that cell lines are not representative of patient proteomic patterns. [ABSTRACT FROM AUTHOR]

Developing type 2 diabetes (T2D) can increase patient risk of developing other common diseases and exacerbate their severity, including diseases that affect bone and joints. Such comorbidity interactions are hard to study in detail by traditional endocrinological methods. Thus, we developed tissue transcript analytical approaches to identify common pathways through which these diseases can interact. We examined RNAseq and microarray transcript datasets from studies of T2D and chronic bone and joint diseases, namely rheumatoid arthritis (RA), osteoarthritis (OA), juvenile idiopathic arthritis (JIA) and low peak bone density, a key osteoporosis (OP) determinant. These datasets contained data from affected individuals and matched controls. Differentially expressed genes (DEGs) for each condition were compared with T2D DEG. Overlapping DEGs (i.e., those common to T2D and a bone or joint condition) were subjected to gene enrichment by pathway analyses and by gene ontology methods, and the results were evaluated by using SNP-disease linkage (dbGaP) and gene-disease association (OMIM) databases that indicate gene involvement in pathologies. By examining gene targets of transcription factors (TFs) and microRNA (miRNAs), we also constructed DEG-TF and DEG-miRNA interactions networks for analysis. We identified strong candidate genes in common pathways, notably including SYK, UCP3, ROR1, PPARG, BUB1, AKT2, ADCY2 and CCR5. The DEG-TF network and DEG-miRNA interactions network analyses revealed a number of TFs (GATA2, FOXC1, USF2, YY1, E2F1, JUN, RELA, CREB1, TFAP2A, NFB1) and miRNAs (mir-335-5p, mir-16-5p, mir-26b-5p, mir-124-3p, mir-218-5p, mir-98-5p, mir-29b-3p, mir-3135b, mir-29c-3p, mir-1-1) that can regulate the identified DEGs at the transcriptional and post-transcriptional levels. Thus this data-driven approach has enabled identification and validation of regulatory factors and cell pathways by which T2D may influence bone and joint conditions, which may suggest new ways to interfere with the pathogenic processes involved.

Background: Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms., Purpose: We systematically explore the capabilities of network-based approaches on natural products, using Panax ginseng as a case study. P. ginseng is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood., Methods: Our study compiled and constructed a network focusing on P. ginseng by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model., Results: We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms., Conclusion: our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results., (© 2024 The Korean Society of Ginseng. Publishing services by Elsevier B.V.)

This article analyses the collaborative leadership of a network-based approach that seeks to subvert the social inequality suffered by the residents of Los Asperones (Málaga, Spain), where approximately one thousand people live in extreme exclusion. The network is made up of various socio-educational associations, schools and social services. The main techniques for collecting information were focus groups, interviews and observations. The sample was representative of all the groups living in the area and of the network members. The data were processed using Atlas.ti software. The results show the foundations, strategies and key challenges of the existing collaborative leadership in the network. The foundations include the commitment shared by the various agents involved; the recognition of the value of the other, and the pre-eminence of the 'we' perspective over the 'I' perspective; symptomatic of a move from an 'ego' leadership to an 'eco' leadership. The main methodological strategies employed were: trust, communication, co-responsibility and shared time and space. The lack of time and political dependencies were identified as the major challenges that could weaken or hinder the success of the network's collaborative action. [ABSTRACT FROM AUTHOR]

As in many autoimmune diseases, the pathogenesis of the antiphospholipid syndrome (APS) is the result of a complex interplay between predisposing genes and triggering environmental factors, leading to a loss of self-tolerance and immune-mediated tissue damage. While the first genetic studies in APS focused primarily on the human leukocytes antigen system (HLA) region, more recent data highlighted the role of other genes in APS susceptibility, including those involved in the immune response and in the hemostatic process. In order to join this intriguing debate, we analyzed the single-nucleotide polymorphisms (SNPs) derived from the whole exome sequencing (WES) of two siblings affected by APS and compared our findings with the available literature. We identified genes encoding proteins involved in the hemostatic process, the immune response, and the phospholipid metabolism (PLA2G6, HSPG2, BCL3, ZFAT, ATP2B2, CRTC3, and ADCY3) of potential interest when debating the pathogenesis of the syndrome. The study of the selected SNPs in a larger cohort of APS patients and the integration of WES results with the network-based approaches will help decipher the genetic risk factors involved in the diverse clinical features of APS.

Government policies aim to address public issues and problems and therefore play a pivotal role in people's lives. The creation of public policies, however, is complex given the perspective of large and diverse stakeholders' involvement, considerable human participation, lengthy processes, complex task specification and the non-deterministic nature of the process. The inherent complexities of the policy process impart challenges for designing a computing system that assists in supporting and automating the business process pertaining to policy setup, which also raises concerns for setting up a tracking service in the policy-making environment. A tracking service informs how decisions have been taken during policy creation and can provide useful and intrinsic information regarding the policy process. At present, there exists no computing system that assists in tracking the complete process that has been employed for policy creation. To design such a system, it is important to consider the policy environment challenges; for this a novel network and goal based approach has been framed and is covered in detail in this paper. Furthermore, smart governance objectives that include stakeholders' participation and citizens' involvement have been considered. Thus, the proposed approach has been devised by considering smart governance principles and the knowledge environment of policy making where tasks are largely dependent on policy makers' decisions and on individual policy objectives. Our approach reckons the human dimension for deciding and defining autonomous process activities at run time. Furthermore, with the network-based approach, so-called provenance data tracking is employed which enables the capture of policy process. [ABSTRACT FROM AUTHOR]

Since the last few years, the Taiwanese government has been promoting the cultural and creative industries, causing the industries in Taiwan to grow rapidly. This study utilizes data envelopment analysis (DEA) to evaluate the cultural and creative promotion performance of cities/counties in Taiwan. Furthermore, the DEA and network-based approach are combined not only to identify the most important inputs/outputs, but also to identify cities/counties that can be treated as benchmarks. This study confirms the effect of the economic gap between urban and rural areas on the promotion and development of the cultural and creative industries in Taiwan. The cultural and creative promotions in the urban areas are found to be superior to those in the rural areas in Taiwan. In addition, the cultural and creative industries in the northern Taiwan are found to be superior to those in the other areas in Taiwan. This study offers some recommendations for managers in cultural and creative departments in their decision-makings; following which will mitigate the gap between resources available in urban and rural areas and facilitate balanced development of the cultural and creative industries in Taiwan. [ABSTRACT FROM PUBLISHER]

Abstract: The cultural and creative industries (CCIs) in Taiwan have gradually contributed to the national economy under the impetus of government policies. We employ a two‐stage data envelopment analysis model with an additive efficiency decomposition approach to measure the profitability and marketability of 22 Taiwanese cultural and creative companies. Furthermore, we employ the network‐based ranking approach to identify benchmark inputs/outputs, and the strengths and weakness of each company. Our empirical results show that the profitability of the cultural and creative companies is better than their marketability. Companies in the industries of publishing, creative life, popular music, and cultural content averagely perform better than those in the other three types of CCIs in terms of profitability. Companies in the creative life industry are on average more efficient than those in the other five types of CCIs in terms of marketability. The profitability/marketability matrix of cultural and creative companies is also presented. [ABSTRACT FROM AUTHOR]

Government policies aim to address public issues and problems and therefore play a pivotal role in people’s lives. The creation of public policies, however, is complex given the perspective of large and diverse stakeholders’ involvement, considerable human participation, lengthy processes, complex task specification and the non-deterministic nature of the process. The inherent complexities of the policy process impart challenges for designing a computing system that assists in supporting and automating the business process pertaining to policy setup, which also raises concerns for setting up a tracking service in the policy-making environment. A tracking service informs how decisions have been taken during policy creation and can provide useful and intrinsic information regarding the policy process. At present, there exists no computing system that assists in tracking the complete process that has been employed for policy creation. To design such a system, it is important to consider the policy environment challenges; for this a novel network and goal based approach has been framed and is covered in detail in this paper. Furthermore, smart governance objectives that include stakeholders’ participation and citizens’ involvement have been considered. Thus, the proposed approach has been devised by considering smart governance principles and the knowledge environment of policy making where tasks are largely dependent on policy makers’ decisions and on individual policy objectives. Our approach reckons the human dimension for deciding and defining autonomous process activities at run time. Furthermore, with the network-based approach, so-called provenance data tracking is employed which enables the capture of policy process.

When people witness or imagine the pain of another person, their nervous system may react as if they were feeling that pain themselves. Early neuroscientific evidence indicates that the firsthand and vicarious experiences of pain share largely overlapping neural structures, which typically correspond to the lateral and medial brain regions that encode the sensory and the affective qualities of pain. Such neural circuitry is highly malleable and allows people to flexibly adjust the empathic behavior depending on social and personal factors. Recent views posit, however, that the brain can be conceptualized as a complex system, in which behavior emerges from the interaction between functionally connected brain regions, organized into large-scale networks. Beyond the classical modular view of the brain, here we suggest that empathic behavior may be understood through a dynamic network-based approach where the cortical circuits associated with the experience of pain flexibly change in order to code self- and other-related emotions and to intrinsically map our mentality to empathetically react to others. [ABSTRACT FROM AUTHOR]

Background: COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed.Purpose: This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases.Methods: A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles.Results: In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks.Conclusion: Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving. [ABSTRACT FROM AUTHOR]

We present a cancer genomic analysis pipeline which takes as input sequencing reads for both germline and tumor genomes and outputs filtered lists of all genetic mutations in the form of short ranked list of the most affected genes in the tumor, using either the Complete Genomics or Illumina platforms. A novel reporting and ranking system has been developed that makes use of publicly available datasets and literature specific to each patient, including new methods for using publicly available expression data in the absence of proper control data. Previously implicated small and large variations (including gene fusions) are reported in addition to probable driver mutations. Relationships between cancer and the sequenced tumor genome are highlighted using a network-based approach that integrates known and predicted protein-protein, protein-TF, and protein-drug interaction data. By using an integrative approach, effects of genetic variations on gene expression are used to provide further evidence of driver mutations. This pipeline has been developed with the aim to be used in assisting in the analysis of pediatric tumors, as an unbiased and automated method for interpreting sequencing results along with identifying potentially therapeutic drugs and their targets. We present results that agree with previous literature and highlight specific findings in a few patients. [ABSTRACT FROM PUBLISHER]

The inherent complexity of supply chains, combined with their economic importance, has prompted research in supply chain management. While there has been a significant amount of work reported in modelling supply chains, the effectiveness of these models in addressing the complex area of supply chain management is questionable. What is giving added urgency to the need for research in this area are the recent developments in communications that offer the promise of connecting suppliers, assemblers and customers in a seamless network of information that can form integrated supply chain management. This paper aims to address the deficiency in modelling by proposing a methodology that models the integrated supply chain as an abstracted network, called a Trans-Net. The Trans-Net approach offers three main potential advantages. First, the multiple participants in a supply chain can be modelled without undue complexity. Second, it is an approach that allows users to examine the interactions between different considerations so that important attributes such as cost and lead-time can be readily calculated and bottleneck areas identified. Third, it applies a mathematical formalism to the problem, with the potential of being relatively straightforward to computerize. [ABSTRACT FROM AUTHOR]

This study aims to measure the ability of 29 countries in producing competitive products and services that fulfill individual needs and improve the level of welfare with less utilization of natural resources. We build a two-stage network production process model to investigate the ecology efficiency and social welfare efficiency of the countries and then further discriminate the efficient countries in post-analysis. The two-stage network directional distance function is applied to assess the efficiencies of countries, and the network-based ranking approach is used to further discriminate the efficient countries following the panel data between the years 2013 and 2016. Results show that Poland and Spain are strongly referenced by other countries in the ecology stage, whereas Bulgaria, the United States, and Sweden are leaders in the social welfare stage. A remarkable observation is an absence of countries' efficiency in both ecology and social welfare efficiencies. Most of the 29 countries have lower efficiency in the social welfare stage than in the ecology stage. This study suggests the strengths and highlights the weaknesses of the countries to help the governments efficiently improve and operate their countries.

As in many autoimmune diseases, the pathogenesis of the antiphospholipid syndrome (APS) is the result of a complex interplay between predisposing genes and triggering environmental factors, leading to a loss of self-tolerance and immune-mediated tissue damage. While the first genetic studies in APS focused primarily on the human leukocytes antigen system (HLA) region, more recent data highlighted the role of other genes in APS susceptibility, including those involved in the immune response and in the hemostatic process. In order to join this intriguing debate, we analyzed the single-nucleotide polymorphisms (SNPs) derived from the whole exome sequencing (WES) of two siblings affected by APS and compared our findings with the available literature. We identified genes encoding proteins involved in the hemostatic process, the immune response, and the phospholipid metabolism (PLA2G6, HSPG2, BCL3, ZFAT, ATP2B2, CRTC3, and ADCY3) of potential interest when debating the pathogenesis of the syndrome. The study of the selected SNPs in a larger cohort of APS patients and the integration of WES results with the network-based approaches will help decipher the genetic risk factors involved in the diverse clinical features of APS. [ABSTRACT FROM AUTHOR]