Hepatocellular carcinoma (HCC) is a highly malignant tumor found in the bile duct epithelial cells, and the second most common tumor of the liver. However, the pivotal roles of most molecules of ...tumorigenesis in HCC are still unclear. Hence, it is essential to detect the tumorigenic mechanism and develop novel prognostic biomarkers for clinical application. The data of HCC mRNA‐seq and clinical information from The Cancer Genome Atlas (TCGA) database were analyzed by weighted gene co‐expression network analysis (WGCNA). Co‐expression modules and clinical traits were constructed by the Pearson correlation analysis, interesting modules were selected and gene ontology and pathway enrichment analysis were performed. Intramodule analysis and protein–protein interaction construction of selected modules were conducted to screen hub genes. In addition, upstream transcription factors and microRNAs of hub genes were predicted by miRecords and NetworkAnalyst database. Afterward, a high connectivity degree of hub genes from two networks was picked out to perform the differential expression validation in the Gene Expression Profiling Interactive Analysis database and Human Protein Atlas database and survival analysis in Kaplan–Meier plotter online tool. By utilizing WGCNA, several hub genes that regulate the mechanism of tumorigenesis in HCC were identified, which was associated with clinical traits including the pathological stage, histological grade, and liver function. Surprisingly, ZWINT, CENPA, RACGAP1, PLK1, NCAPG, OIP5, CDCA8, PRC1, and CDK1 were identified statistically as hub genes in the blue module, which were closely implicated in pathological T stage and histologic grade of HCC. Moreover, these genes also were strongly associated with the HCC cell growth and division. Network and survival analyses found that nine hub genes may be considered theoretically as indicators to predict the prognosis of patients with HCC or clinical treatment target, it will be necessary for basic experiments and large‐scale cohort studies to validate further.
This article discussed how to screen the biomarkers of hepatocellular carcinoma (HCC) by the weighted gene co‐expression network analysis (WGCNA) and upstream analysis, and identify the hub genes by the differential expression and survival analysis. Finally, nine identified genes were regarded as potential biomarkers, which were associated with HCC cell growth and division. It may serve as novel prognostic markers and treatment targets theoretically, but it is necessary to validate further with basic experiments and large‐scale cohort studies’ support.
The non-independence of social network data is a cause for concern among behavioural ecologists conducting social network analysis. This has led to the adoption of several permutation-based methods ...for testing common hypotheses. One of the most common types of analysis is nodal regression, where the relationships between node-level network metrics and nodal covariates are analysed using a permutation technique known as node-label permutations. We show that, contrary to accepted wisdom, node-label permutations do not automatically account for the non-independences assumed to exist in network data, because regression-based permutation tests still assume exchangeability of residuals. The same assumption also applies to the quadratic assignment procedure (QAP), a permutation-based method often used for conducting dyadic regression. We highlight that node-label permutations produce the same
p
-values as equivalent parametric regression models, but that in the presence of non-independence, parametric regression models can also produce accurate effect size estimates. We also note that QAP only controls for a specific type of non-independence between edges that are connected to the same nodes, and that appropriate parametric regression models are also able to account for this type of non-independence. Based on this, we suggest that standard parametric models could be used in the place of permutation-based methods. Moving away from permutation-based methods could have several benefits, including reducing over-reliance on
p-
values, generating more reliable effect size estimates, and facilitating the adoption of causal inference methods and alternative types of statistical analysis.
To ensure online collaborative learning meets the intended pedagogical goals (is actually collaborative and stimulates learning), mechanisms are needed for monitoring the efficiency of online ...collaboration. Various studies have indicated that social network analysis can be particularly effective in studying students' interactions in online collaboration. However, research in education has only focused on the theoretical potential of using SNA, not on the actual benefits they achieved. This study investigated how social network analysis can be used to monitor online collaborative learning, find aspects in need of improvement, guide an informed intervention, and assess the efficacy of intervention using an experimental, observational repeated-measurement design in three courses over a full-term duration. Using a combination of SNA-based visual and quantitative analysis, we monitored three SNA constructs for each participant: the level of interactivity, the role, and position in information exchange, and the role played by each participant in the collaboration. On the group level, we monitored interactivity and group cohesion indicators. Our monitoring uncovered a non-collaborative teacher-centered pattern of interactions in the three studied courses as well as very few interactions among students, limited information exchange or negotiation, and very limited student networks dominated by the teacher. An intervention based on SNA-generated insights was designed. The intervention was structured into five actions: increasing awareness, promoting collaboration, improving the content, preparing teachers, and finally practicing with feedback. Evaluation of the intervention revealed that it has significantly enhanced student-student interactions and teacher-student interactions, as well as produced a collaborative pattern of interactions among most students and teachers. Since efficient and communicative activities are essential prerequisites for successful content discussion and for realizing the goals of collaboration, we suggest that our SNA-based approach will positively affect teaching and learning in many educational domains. Our study offers a proof-of-concept of what SNA can add to the current tools for monitoring and supporting teaching and learning in higher education.
Many real-world relations can be represented by signed networks with positive and negative links, as a result of which signed network analysis has attracted increasing attention from multiple ...disciplines. With the increasing prevalence of social media networks, signed network analysis has evolved from developing and measuring theories to mining tasks. In this article, we present a review of mining signed networks in the context of social media and discuss some promising research directions and new frontiers. We begin by giving basic concepts and unique properties and principles of signed networks. Then we classify and review tasks of signed network mining with representative algorithms. We also delineate some tasks that have not been extensively studied with formal definitions and also propose research directions to expand the field of signed network mining.
•Urban energy consumption was assessed from three different perspectives.•A new concept called controlled energy was developed from network analysis.•Embodied energy and controlled energy consumption ...of Beijing were compared.•The integration of all three perspectives will elucidate sustainable energy use.
Energy consumption has always been a central issue for sustainable urban assessment and planning. Different forms of energy analysis can provide various insights for energy policy making. This paper brought together three approaches for energy consumption accounting, i.e., energy flow analysis (EFA), input–output analysis (IOA) and ecological network analysis (ENA), and compared their different perspectives and the policy implications for urban energy use. Beijing was used to exemplify the different energy analysis processes, and the 42 economic sectors of the city were aggregated into seven components. It was determined that EFA quantifies both the primary and final energy consumption of the urban components by tracking the different types of fuel used by the urban economy. IOA accounts for the embodied energy consumption (direct and indirect) used to produce goods and services in the city, whereas the control analysis of ENA quantifies the specific embodied energy that is regulated by the activities within the city’s boundary. The network control analysis can also be applied to determining which economic sectors drive the energy consumption and to what extent these sectors are dependent on each other for energy. So-called “controlled energy” is a new concept that adds to the analysis of urban energy consumption, indicating the adjustable energy consumed by sectors. The integration of insights from all three accounting perspectives further our understanding of sustainable energy use in cities.
Research has repeatedly proven the importance of social interactions in online learning contexts such as Massive Open Online Courses (MOOCs), where learners often reported isolation and a lack of ...peer support. Previous studies of social presence suggested that the ways learners present themselves socially online affect their learning outcomes. In order to further understand the role of learners' social presence, this study attempts to examine the relationship between social presence and learners' prestige in the learner network of a MOOC. An automated text classification model based on the latest machine learning techniques was developed to identify different social presence indicators from forum posts, while two metrics (in-degree and authority score) in social network analysis (SNA) were used to measure learners' prestige in the learner network. Results revealed that certain social presence indicators such as Asking questions, Expressing gratitude, Self-disclosure, Sharing resources and Using Vocatives have positive correlations with learners' prestige, while the expressions of Disagreement/doubts/criticism and Negative emotions were counterproductive to learners' prestige. The findings not only reinforce the importance of social presence in online learning, but also shed light on the strategies of leveraging social presence to improve individual's prestige in social learning contexts like MOOCs.
•Certain social presence predicts higher network prestige in MOOC discussion forum.•An automated text classification model was developed to identify social presence.•Social presence can be used to increase engagement in the learner community of MOOC.
Computational Social Science and Sociology Edelmann, Achim; Wolff, Tom; Montagne, Danielle ...
Annual review of sociology,
07/2020, Letnik:
46, Številka:
1
Journal Article
Recenzirano
Odprti dostop
The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel ...sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via bibliometric analysis and in-depth analysis of the following subfields where this new work is appearing most rapidly: (
a
) social network analysis and group formation; (
b
) collective behavior and political sociology; (
c
) the sociology of knowledge; (
d
) cultural sociology, social psychology, and emotions; (
e
) the production of culture; (
f
) economic sociology and organizations; and (
g
) demography and population studies. Our review reveals that sociologists are not only at the center of cutting-edge research that addresses longstanding questions about human behavior but also developing new lines of inquiry about digital spaces as well. We conclude by discussing challenging new obstacles in the field, calling for increased attention to sociological theory, and identifying new areas where computational social science might be further integrated into mainstream sociology.
To improve the flow of quality information and combat fake news on social media, it is essential to identify the origins and evolution patterns of false information. However, scholarship dedicated to ...this area is lacking. Using a recent development in the field of computational network science (i.e., evolution tree analysis), this study examined this issue in the context of the 2016 US presidential election. By retrieving 307,738 tweets about 30 fake and 30 real news stories, we examined the root content, producers of original source, and evolution patterns. The findings revealed that root tweets about fake news were mostly generated by accounts from ordinary users, but they often included a link to non-credible news websites. Additionally, we observed significant differences between real and fake news stories in terms of evolution patterns. In our evolution tree analysis, tweets about real news showed wider breadth and shorter depth than tweets about fake news. The results also indicated that tweets about real news spread widely and quickly, but tweets about fake news underwent a greater number of modifications in content over the spreading process.
•The evolution tree analysis was performed on tweets about real and fake news stories.•Most root tweets about fake news were generated by ordinary users.•Root tweets about fake news often included a link to non-credible news websites.•Tweets about fake news undergo frequent modifications in the original content over the spreading process.•Tweets about real news spread widely without modifications in the original content.
The purpose of this paper is to analyse the application of Social Network Analysis (SNA) to the Italian tourism system. The research question is: do relationships among tourist enterprises affect the ...organizational asset of the Italian travel system? The research takes as unit of analysis the Italian travel agencies and tour operators system and represents quite a significant disclosure for organizational theses because it offers a different view over the structure and governance of a hospitality intermediaries' network. SNA is helpful indetecting genuine proficiency and therefore in foreseeing possible losses determined by poor or inefficient configurations. Furthermore, it will help delineate new roles within the organizational networks and evaluate the relation between formal and informal organizational structures. This paper provides a structural analysis of the Italian travel agencies network and highlights its self-organization characteristics (typical of a complex system) that lead to the development of informal communities. The methods of network science proved useful and effective and, together with more traditional approaches and a qualitative knowledge of the system, can provide a deeper and more extensive understanding of the system.
In the field of learning analytics, mining the regularities of social interaction and cognitive processing have drawn increasing attention. Nevertheless, in MOOCs, there is a lack of investigations ...on the combination of social and cognitive behavioral patterns. To fill in this gap, this study aimed to uncover the relationship between social interaction, cognitive processing, and learning achievements in a MOOC discussion forum. Specifically, we collected the 3925 participants’ forum data throughout 16 weeks. Social network analysis and epistemic network analysis were jointly adopted to investigate differences in social interaction, cognitive processing between two achievement groups, and the differences in cognitive processing networks between two types of communities. Finally, moderation analysis was employed to examine the moderating effect of community types between cognitive processing and learning achievements. Results indicated that: (1) the high- and low-achieving groups presented significant differences in terms of degree, betweenness, and eigenvector centrality; (2) the stronger cognitive connections were found within the high-achieving group and the instructor-led community; (3) the cognitive processing indicators including insight, discrepancy, and tentative were significantly negative predictors of learning achievements, whereas inhibition and exclusive were significantly positive predictors; (4) the community type moderated the relationship between cognitive processing and learning achievements.
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