Social network Analysis-based classifier (SNAc): A case study on time course gene expression data

Assessing territorial space conflicts in the coastal zone of Wenzhou, China: A land-sea interaction perspective

Territorial space conflicts (TSCs) in coastal zones stem from the intricate interaction between the land-sea dual system, significantly impacting the sustainable development of these areas. To accurately identify TSCs, our study proposed a theoretical framework based on a land-sea interaction perspective. We also assessed TSCs using the territorial space conflicts index and a social network analysis model. We demonstrated the proposed spatial conflicts assessment methodology through a case study of Wenzhou, a typical city on the east coast of China. Our results indicate that the distribution of TSCs gradually decreased from the coastal zone to the inland zone, with significant variation in the distribution of different conflict types across different zones. The findings also reveal that territorial space use had diverse impacts on the space conflict network, making it urgent to take targeted measures. In the future, it is crucial to comprehensively consider the overall pattern and distribution characteristics of current TSCs, as well as the spatial spillover effect of the overall network, to develop targeted coping strategies and regulation mechanisms that promote the integration and high-quality development of coastal territorial space systems. To maintain a sustainable coastal zone process, we proposed a set of optimization paths for alleviating TSCs and promoting the coordinated development of land and sea regions in China based on our study.

Biomarker detection using corrected degree of domesticity in hybrid social network feature selection for improving classifier performance

BACKGROUND

Dimension reduction, especially feature selection, is an important step in improving classification performance for high-dimensional data. Particularly in cancer research, when reducing the number of features, i.e., genes, it is important to select the most informative features/potential biomarkers that could affect the diagnostic accuracy. Therefore, researchers continuously try to explore more efficient ways to reduce the large number of features/genes to a small but informative subset before the classification task. Hybrid methods have been extensively investigated for this purpose, and research to find the optimal approach is ongoing. Social network analysis is used as a part of a hybrid method, although there are several issues that have arisen when using social network tools, such as using a single environment for computing, constructing an adjacency matrix or computing network measures. Therefore, in our study, we apply a hybrid feature selection method consisting of several machine learning algorithms in addition to social network analysis with our proposed network metric, called the corrected degree of domesticity, in a single environment, R, to improve the support vector machine classifier's performance. In addition, we evaluate and compare the performances of several combinations used in the different steps of the method with a simulation experiment.

RESULTS

The proposed method improves the classifier's performance compared to using the whole feature set in all the cases we investigate. Additionally, in terms of the area under the receiver operating characteristic (ROC) curve, our approach improves classification performance compared to several approaches in the literature.

CONCLUSION

When using the corrected degree of domesticity as a network degree centrality measure, it is important to use our correction to compare nodes/features with no connection outside of their community since it provides a more accurate ranking among the features. Due to the nature of the hybrid method, which includes social network analysis, it is necessary to investigate possible combinations to provide an optimal solution for the microarray data used in the research.

Patent mining on soil pollution remediation technology from the perspective of technological trajectory

Recent years have seen a marked growth in soil environmental problems, however, the research & development (R&D) direction of soil pollution remediation technology (SPRT) for addressing related challenges to the global ecosystem is still unclear. Patent is the most effective carrier of technological information. Therefore, this study investigates the status and future direction of SPRT through the analysis and mining of 14,475 patents from 1971 to 2020. In 2006-2020, 14,435 SPRT patents (79% of the total) were published, which is in the development stage. By measuring the proportion of high-value patents, determined by the ratio of the number of patent families containing two or more patents (PF2) to that containing at least one patent (PF1), we found that United States (PF2/PF1 = 0.711), Japan (PF2/PF1 = 0.500), and South Korea (PF2/PF1 = 0.431) hold a monopoly. International patent organizations serve as a bridge for technology transfer. Patent CN101947539-A measured by structural hole index (Effective size = 98.194, Efficiency = 0.926) has the most significant technological influence. Therefore, in order to accomplish the technological transition and improve the soil remediation capacity, more attention should be paid to the microbial-assisted phytoremediation technology related to inorganic pollutants, hyperaccumulators and stabilizers. Additionally, patents CN102834190-A (Effective size = 23.930, Efficiency = 0.855, Constraint = 0.141, Hierarchy = 0.089) and CN105855289 (Effective size = 21.453, Efficiency = 0.795 Constraint = 0.149, Hierarchy = 0.086) are both at the location of structural holes. So, more research should be carried out on green and cost-effective solutions for reducing organic pollutants in soil remediation. The current study identifies opportunities for innovations and breakthroughs in SPRT and offers relevant information on technological development prospects.

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review

Background

Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging.

Methods

Systematic searches through eight databases were conducted for literature published in 2014–2020 on MS and specified ML algorithms.

Results

Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms.

Conclusions

ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.

Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier

We propose a new type of supervised visual machine learning classifier, GSNAc, based on graph theory and social network analysis techniques. In a previous study, we employed social network analysis techniques and introduced a novel classification model (called Social Network Analysis-based Classifier—SNAc) which efficiently works with time-series numerical datasets. In this study, we have extended SNAc to work with any type of tabular data by showing its classification efficiency on a broader collection of datasets that may contain numerical and categorical features. This version of GSNAc simply works by transforming traditional tabular data into a network where samples of the tabular dataset are represented as nodes and similarities between the samples are reflected as edges connecting the corresponding nodes. The raw network graph is further simplified and enriched by its edge space to extract a visualizable ‘graph classifier model—GCM’. The concept of the GSNAc classification model relies on the study of node similarities over network graphs. In the prediction step, the GSNAc model maps test nodes into GCM, and evaluates their average similarity to classes by employing vectorial and topological metrics. The novel side of this research lies in transforming multidimensional data into a 2D visualizable domain. This is realized by converting a conventional dataset into a network of ‘samples’ and predicting classes after a careful and detailed network analysis. We exhibit the classification performance of GSNAc as an effective classifier by comparing it with several well-established machine learning classifiers using some popular benchmark datasets. GSNAc has demonstrated superior or comparable performance compared to other classifiers. Additionally, it introduces a visually comprehensible process for the benefit of end-users. As a result, the spin-off contribution of GSNAc lies in the interpretability of the prediction task since the process is human-comprehensible; and it is highly visual.

Spatiotemporal Dynamics of Direct Carbon Emission and Policy Implication of Energy Transition for China’s Residential Consumption Sector by the Methods of Social Network Analysis and Geographically Weighted Regression

As China’s second largest energy-use sector, residential consumption has a great potential for carbon dioxide (CO2) reduction and energy saving or transition. Thus, here, using the methods of social network analysis (SNA) and geographically weighted regression (GWR), we investigated the spatiotemporal evolution characteristics of China’s residential CO2 emissions (RCEs) from direct energy use and proposed some policy suggestions for regional energy transition. (1) From 2000 to 2019, the total direct RCEs rose from 396.32 Mt to 1411.69 Mt; the consumption of electricity and coal were the primary sources. Controlling coal consumption and increasing the proportion of electricity generated from renewable energy should be the effective way of energy transition. (2) The spatial associations of direct RCEs show an obvious spatial network structure and the number of associations is increasing. Provinces with a higher level of economic development (Beijing, Shanghai, and Jiangsu) were at the center of the network and classified as the net beneficiary cluster in 2019. These provinces should be the priority areas of energy transition. (3) The net spillover cluster (Yunnan, Shanxi, Xinjiang, Gansu, Qinghai, Guizhou) is an important area to develop clean energy. People in this cluster should be encouraged to use more renewable energy. (4) GDP and per capita energy consumption had a significant positive influence on the growth of direct RCEs. Therefore, the national economy should grow healthily and sustainably to provide a favorable economic environment for energy transition. Meanwhile, residential consumption patterns should be greener to promote the use of clean energy.

A novel approach for the analysis of time-course gene expression data based on computing with words

In this paper, a novel approach is proposed for the analysis of time-course gene expression data based on the path-breaking work of Zadeh, Computing with Words. This method can automatically discover the patterns of temporal gene expression profile in terms of two distinguishing descriptions: linguistic description that is understandable and interpretable for human inference; and type-2 fuzzy description that is suitable for robust machine inference in the presence of uncertainty. In contrast to conventional static data mining methods which focus on the steady-state gene expression levels, the proposed scheme is a new time-series pattern mining technique for dynamical modeling of gene expression. To evaluate the performance of this paradigm, it is applied to a case study dataset from Gene Expression Omnibus (GEO) which includes the temporal transcriptional profile of human colon cancer cells. The goal is to investigate the pharmacodynamics of two anticancer drugs. The transient and steady-state analysis of the transcriptional response clearly demonstrates the ability of the proposed approach to reveal the pharmacodynamical effects of drug type and dosage on the expression of genes.

Public opinion information dissemination in mobile social networks – taking Sina Weibo as an example

Purpose

Social media, especially microblog, has become one of the most popular platforms for public opinion dissemination. However, so far few studies have been conducted to explore information dissemination under the mobile environment. This paper aims to introduce the approach to analyze the public opinion information dissemination in mobile social networks.

Design/methodology/approach

This paper chooses “network attack” as the research topic and extracts 23,567 relevant messages from Sina Microblogs to study the structure of nodes for public opinion dissemination and the characteristics of propagation paths on mobile internet. Public opinion dissemination is compared on both mobile and non-mobile terminals.

Findings

The results reveal the characteristics of public opinion dissemination in mobile environment and identify three patterns of information propagation path. This study concludes that public opinion on mobile internet propagates more widely and efficiently and generates more impact than that on the non-mobile internet.

Social implications

The methods used in this study can be useful for the government and other organizations to analyze and identify problems in online information dissemination.

Originality/value

This paper explores the mechanism of public opinion dissemination on mobile internet in China and further investigates how to improve public opinion management through a case study related to “network attack.”