The next‐generation <i>K</i>‐means algorithm

Demidenko, Eugene

doi:10.1002/sam.11379

Cited by 16 publications

(7 citation statements)

References 33 publications

(38 reference statements)

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“…Reads per kilobase of exon model per million mapped reads (RPKM) was used to evaluate the genes expression in each sample. Clustering of organs in control group was performed using scaled RPKM by K-means algorithm (Demidenko, 2018 ) with 10,000 iterations. DEGs between infected and control were identified by edgeR (Robinson et al, 2010 ) in R program using the threshold ∣log 2 (fold change)∣ ≥ 1 and P value < 0.05.…”

Section: Methodsmentioning

confidence: 99%

Differential transcriptomic landscapes of multiple organs from SARS-CoV-2 early infected rhesus macaques

Gao

Deng

et al. 2022

Protein Cell

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SARS-CoV-2 infection causes complicated clinical manifestations with variable multi-organ injuries, however, the underlying mechanism, in particular immune responses in different organs, remains elusive. In this study, comprehensive transcriptomic alterations of 14 tissues from rhesus macaque infected with SARS-CoV-2 were analyzed. Compared to normal controls, SARS-CoV-2 infection resulted in dysregulation of genes involving diverse functions in various examined tissues/organs, with drastic transcriptomic changes in cerebral cortex and right ventricle. Intriguingly, cerebral cortex exhibited a hyperinflammatory state evidenced by significant upregulation of inflammation response-related genes. Meanwhile, expressions of coagulation, angiogenesis and fibrosis factors were also up-regulated in cerebral cortex. Based on our findings, neuropilin 1 (NRP1), a receptor of SARS-CoV-2, was significantly elevated in cerebral cortex post infection, accompanied by active immune response releasing inflammatory factors and signal transmission among tissues, which enhanced infection of the central nervous system (CNS) in a positive feedback way, leading to viral encephalitis. Overall, our study depicts a multi-tissue/organ transcriptomic landscapes of rhesus macaque with early infection of SARS-CoV-2, and provides important insights into the mechanistic basis for COVID-19-associated clinical complications.

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Section: Methodsmentioning

confidence: 99%

Differential transcriptomic landscapes of multiple organs from SARS-CoV-2 early infected rhesus macaques

Gao

Deng

et al. 2022

Protein Cell

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“…K-means methodology is a machine-learning technique that identifies and groups analysis units (in our case BHA) based on their similarities of characteristics. 28 K-means methodology will be used to identify clusters of SARS-CoV-2 incidence by BHA, taking into account the rest of the variables described above. In addition, we will identify the spatial-temporal cluster of SARS-CoV-2 infection incidence and seroprevalence by BHA using SaTScan .…”

Section: Methods and Analysismentioning

confidence: 99%

Monitoring of SARS-CoV-2 seroprevalence among primary healthcare patients in the Barcelona Metropolitan Area: the SeroCAP sentinel network protocol

et al. 2022

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IntroductionSARS-CoV-2 seroprevalence studies are currently being recommended and implemented in many countries. Forming part of the COVID-19 monitoring and evaluation plan of the Catalan Government Health Department, our network aims to initiate a primary healthcare sentinel monitoring system as a surrogate of SARS-CoV-2 exposure in the Barcelona Metropolitan Area.Methods and analysisThe seroCAP is a serial cross-sectional study, which will be performed in the Barcelona Metropolitan Area to estimate antibodies against SARS-CoV-2. From February 2021 to March 2022, the detection of serum IgG antibodies against SARS-CoV-2 trimeric spike protein will be performed on a monthly basis in blood samples collected for diverse clinical purposes in three reference hospitals from the three Barcelona healthcare areas (BCN areas). The samples (n=2588/month) will be from patients attended by 30 primary healthcare teams at 30 basic healthcare areas (BHA). A lab software algorithm will systematically select the samples by age and sex. Seroprevalence will be estimated and monitored by age, sex, BCN area and BHA. Descriptive and cluster analysis of the characteristics and distribution of SARS-CoV-2 infections will be performed. Sociodemographic, socioeconomic and morbidity-associated factors will be determined using logistic regression. We will explore the association between seroprevalence, SARS-CoV-2 confirmed cases and the implemented measures using interrupted time series analysis.Ethics and disseminationEthical approval was obtained from the University Institute Foundation for Primary Health Care Research Jordi Gol i Gurina ethics committee. An informed consent is not required regarding the approval of the secondary use of biological samples within the framework of the COVID-19 pandemic. A report will be generated quarterly. The final analysis, conclusions and recommendations will be shared with the stakeholders and communicated to the general public. Manuscripts resulting from the network will be submitted for publication in peer-reviewed journals.

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“…t -SNE dimension reduction paved the way for subsequent clustering analysis. In this study, we applied unsupervised machine learning k-means clustering [ 42 ]. Note that we created 6 major categories of content features for our own manual content coding effort.…”

Section: Methodsmentioning

confidence: 99%

A Novel Machine Learning Framework for Comparison of Viral COVID-19–Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis

Chen¹,

Zhou²,

Song³

et al. 2021

J Med Internet Res

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Background Social media plays a critical role in health communications, especially during global health emergencies such as the current COVID-19 pandemic. However, there is a lack of a universal analytical framework to extract, quantify, and compare content features in public discourse of emerging health issues on different social media platforms across a broad sociocultural spectrum. Objective We aimed to develop a novel and universal content feature extraction and analytical framework and contrast how content features differ with sociocultural background in discussions of the emerging COVID-19 global health crisis on major social media platforms. Methods We sampled the 1000 most shared viral Twitter and Sina Weibo posts regarding COVID-19, developed a comprehensive coding scheme to identify 77 potential features across six major categories (eg, clinical and epidemiological, countermeasures, politics and policy, responses), quantified feature values (0 or 1, indicating whether or not the content feature is mentioned in the post) in each viral post across social media platforms, and performed subsequent comparative analyses. Machine learning dimension reduction and clustering analysis were then applied to harness the power of social media data and provide more unbiased characterization of web-based health communications. Results There were substantially different distributions, prevalence, and associations of content features in public discourse about the COVID-19 pandemic on the two social media platforms. Weibo users were more likely to focus on the disease itself and health aspects, while Twitter users engaged more about policy, politics, and other societal issues. Conclusions We extracted a rich set of content features from social media data to accurately characterize public discourse related to COVID-19 in different sociocultural backgrounds. In addition, this universal framework can be adopted to analyze social media discussions of other emerging health issues beyond the COVID-19 pandemic.

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The next‐generation K‐means algorithm

Cited by 16 publications

References 33 publications

Differential transcriptomic landscapes of multiple organs from SARS-CoV-2 early infected rhesus macaques

Differential transcriptomic landscapes of multiple organs from SARS-CoV-2 early infected rhesus macaques

Monitoring of SARS-CoV-2 seroprevalence among primary healthcare patients in the Barcelona Metropolitan Area: the SeroCAP sentinel network protocol

A Novel Machine Learning Framework for Comparison of Viral COVID-19–Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis

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