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Screening of periodontitis-related diagnostic biomarkers based on weighted gene correlation network analysis and machine algorithms

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TECHNOLOGY AND HEALTH CARE 2022年第5期30卷 1209-1221页

作者： Ji, Juanjuan Li, Xudong Zhua, Yaling Wang, Rui Yang, Shuang Peng, Bei Zhoe, Zhi Yunnan Univ Affiliated Hosp Peoples Hosp Yunnan Prov 2 Dept Stomatol 176 Qingnian Rd Kunming Yunnan Peoples R China Kunming Med Univ Affiliated Stomatol Hosp Dept Prosthodont Kunming Yunnan Peoples R China

BACKGROUND: Periodontitis is a common oral immune inflammatory disease and early detection plays an important role in its prevention and progression. However, there are no accurate biomarkers for early diagnosis. OBJECTIVE: This study screened periodontitis-related diagnostic biomarkers based on weighted gene correlation network analysis and machine algorithms. METHODS: Transcriptome data and sample information of periodontitis and normal samples were obtained from the Gene Expression Omnibus (GEO) database, and key genes of disease-related modules were obtained by bioinformatics. The key genes were subjected to Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and 5 machine algorithms: Logistic Regression (LR), Random Forest (RF), Gradient Boosting Decisio Tree (GBDT), Extreme Gradient Boosting (XGBoost), and Support Vector machine (SVM). Expression and correlation analysis were performed after screening the optimal model and diagnostic biomarkers. RESULTS: A total of 47 candidate genes were obtained, and the LR model had the best diagnostic efficiency. The COL15A1, ICAM2, SLC15A2, and PIP5K1B were diagnostic biomarkers for periodontitis, and all of which were upregulated in periodontitis samples. In addition, the high expression of periodontitis biomarkers promotes positive function with immune cells. CONCLUSION: COL15A1, ICAM2, SLC15A2 and PIP5K1B are potential diagnostic biomarkers of periodontitis.

关键词： Periodontitis WGCNA machine algorithms diagnostic biomarkers

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Identification of potential biomarkers for atrial fibrillation and stable coronary artery disease based on WGCNA and machine algorithms

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BMC CARDIOVASCULAR DISORDERS 2024年第1期24卷 401页

作者： Wu, Ke Chen, Hao Li, Fan Meng, Xiangjuan Chen, Lin Li, Nannan Qingdao Univ Affiliated Taian City Cent Hosp Dept Cardiol Tai An 271000 Peoples R China Shandong Univ Jinan 250012 Peoples R China Qingdao Univ Affiliated Taian City Cent Hosp Intens Care Dept Tai An 271000 Peoples R China Qingdao Univ Affiliated Taian City Cent Hosp Dept Intervent Radiol Tai An 271000 Peoples R China Qingdao Univ Affiliated Taian City Cent Hosp Med Imaging Dept Tai An 271000 Peoples R China Qingdao Univ Affiliated Taian City Cent Hosp Dept Resp & Crit Care 29 Longtan Rd Tai An 27100 Shandong Peoples R China

BackgroundPatients with atrial fibrillation (AF) often have coronary artery disease (CAD), but the biological link between them remains unclear. This study aims to explore the common pathogenesis of AF and CAD and identify common *** expression profiles for AF and stable CAD were downloaded from the Gene Expression Omnibus database. Overlapping genes related to both diseases were identified using weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Hub genes were then identified using the machine learning algorithm. Immune cell infiltration and correlations with hub genes were explored, followed by drug predictions. Hub gene expression in AF and CAD patients was validated by real-time *** obtained 28 common overlapping genes in AF and stable CAD, mainly enriched in the PI3K-Akt, ECM-receptor interaction, and relaxin signaling pathway. Two hub genes, COL6A3 and FKBP10, were positively correlated with the abundance of MDSC, plasmacytoid dendritic cells, and regulatory T cells in AF and negatively correlated with the abundance of CD56dim natural killer cells in CAD. The AUCs of COL6A3 and FKBP10 were all above or close to 0.7. Drug prediction suggested that collagenase clostridium histolyticum and ocriplasmin, which target COL6A3, may be potential drugs for AF and stable CAD. Additionally, COL6A3 and FKBP10 were upregulated in patients with AF and ***6A3 and FKBP10 may be key biomarkers for AF and CAD, providing new insights into the diagnosis and treatment of this disease.

关键词： Atrial fibrillation Stable coronary artery disease Weighted gene co-expression network analysis machine algorithms Biomarkers

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Determination of growth and developmental stages in hand-wrist radiographs: Can fractal analysis in combination with artificial intelligence be used?

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JOURNAL OF OROFACIAL ORTHOPEDICS-FORTSCHRITTE DER KIEFERORTHOPADIE 2024年第SUPPL 2期85卷 11-15页

作者： Gonca, Merve Sert, Mehmet Fatih Gunacar, Dilara Nil Kose, Taha Emre Beser, Busra Recep Tayyip Erdogan Univ Fac Dent Dept Orthodont Menderes Blvd 612 TR-53020 Rize Turkiye Gaziantep Univ Dept Business Adm Quantitat Methods Gaziantep Turkiye Recep Tayyip Erdogan Univ Fac Dent Dept Oral & Maxillofacial Radiol Rize Turkiye

Purpose: The goal of this work was to assess the classification of maturation stage using artificial intelligence (AI) classifiers. Methods: Hand-wrist radiographs (HWRs) from 1067 individuals aged between 7 and 18 years were included. Fifteen regions of interest were selected for fractal dimension (FD) analysis. Five predictive models with different inputs were created (model 1: only FD;model 2: FD and Chapman sesamoid stage;model 3: FD, age, and sex;model 4: FD, Chapman sesamoid stage, age, and sex;model 5: Chapman sesamoid stage, age, and sex). The target diagnoses were accelerating growth velocity, very high growth velocity, and decreasing growth velocity. Four AI algorithms were applied: multilayer perceptron (MLP), support vector machine (SVM), gradient boosting machine (GBM) and C 5.0 decision tree classifier. Results: All AI algorithms except for C 5.0 yielded similar overall predictive accuracies for the five models. In order from lowest to highest, the predictive accuracies of the models were as follows: model 1 < model 3 < model 2 < model 5 < model 4. The highest overall F1 score, which was used instead of accuracy especially for models with unbalanced data, was obtained for models 1, 2, and 3 based on SVM, for model 4 based on MLP, and for model 5 based on C 5.0. Adding Chapman sesamoid stage, chronologic age, and sex as additional inputs to the FD values significantly increased the F1 score. Conclusion: Applying FD analysis to HWRs is not sufficient to predict maturation stage in growing patients but can be considered a growth rate prediction method if combined with the Chapman sesamoid stage, age, and sex.

关键词： machine algorithms Artificial intelligence Fractal analysis machine learning Orthodontics

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Research on the Technology of Generating Parametric Continuous Images of Comic Strip in Virtual Reality Environment Based on machine Algorithm 3

Research on the Technology of Generating Parametric Continuo...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Yuxin, Xia Jiayi, Cai Xiao, Zhang Minghui, Wang Feiyan, Fu Wei, Wei Yan, Li Jingyao, Qin Shenyang Aerospace University Shenyang China

ISBN: (纸本)9798350341546

This thesis explores the feasibility of parametric continuity image generation for comic strips by combining virtual reality technology with machine learning algorithms. In order to promote the development and inheritance of comic strips, we propose to establish an intelligent database of continuous virtual images of comic strips, and use virtual reality technology to apply and experience real users. At the same time, the flexibility and adaptability of machine learning algorithms enable them to be deployed in the analysis, classification and discovery of comic strips by integrating differentiation and acquisition biometric methods. Through the parametric continuous image generated based on machine learning algorithm, we can adjust different parameter settings to achieve the generation of different styles and effects to meet the personalized needs of users. This study holds that the combination of virtual reality and machine learning provides an indispensable path for the modern application of excellent traditional culture, so as to protect and inherit the treasures of Chinese comic strips. Keywords: machine algorithms, Virtual reality, computer vision, parameterization, continuous images. © 2023 IEEE.

关键词： comic strip continuous images machine algorithms parameterization virtual reality

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Digital Authoritarianism: Conceptual Foundations and a Model of Legitimation

VOPROSY FILOSOFII

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VOPROSY FILOSOFII 2022年第8期2022卷 45-54页

作者： Yakovlev, Maksim, V Lomonosov Moscow State Univ Fac Philosophy Dept Philosophy Polit & Law 1 Leninskie GoryGSP 1 Moscow 119991 Russia

One of the important effects of digital transformation is the expansion of digital authoritarianism, which causes a noticeable transformation at all levels (global, national, local) in different socio- political systems. The article defines the concept, philosophical foundations, and model of legitimation of digital and network authoritarianism. The author substantiates the thesis that intelligent technical systems in autocracies support, first of all, the inviolability of the regime on the basis of control over big data, exploitation of sadomasochistic tendencies in society, propaganda through new media and social networks, mechanisms of instrumental and pragmatic consent. Together, these tools create a specific interface of the digital world the emblems of which are passivity and fear, and the installation is a blind belief that what is happening is the norm. It is suggested that as a result of the expansion of authoritarianism in cyberspace, many citizens are forced to independently protect themselves and their property by digital means such as VPN technologies and "onion routing" or using a Hidden Network (Darknet). The digital confrontation between ordinary users and the ruling class (for example, in the form of street protests organized through social networks and messengers) intensifies at the intersection of virtual and material realities. Reflection on these phenomena allows us to give well-founded answers to the questions about the nature and direction of digital transformation in the sphere of state and law, to make reasonable forecasts, to help the public understand the grounds and methods of controlling influence by which the government is held, which programs the consciousness and behavior of citizens.

关键词： machine algorithms internet cyberspace model of legitimation noopolitik network authoritarianism philosophical foundations digital authoritarianism digital transformation

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Potential therapeutic targets for COVID-19 complicated with pulmonary hypertension: a bioinformatics and early validation study

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Hou, Qingbin Jiang, Jinping Na, Kun Zhang, Xiaolin Liu, Dan Jing, Quanmin Yan, Chenghui Han, Yaling Gen Hosp Northern Theater Command Cardiovasc Res Inst State Key Lab Frigid Zone Cardiovasc Dis Dept Cardiol Shenyang Peoples R China China Med Univ Shengjing Affiliated Hosp Dept Cardiol Shenyang Peoples R China

Coronavirus disease (COVID-19) and pulmonary hypertension (PH) are closely correlated. However, the mechanism is still poorly understood. In this article, we analyzed the molecular action network driving the emergence of this event. Two datasets (GSE113439 and GSE147507) from the GEO database were used for the identification of differentially expressed genes (DEGs).Common DEGs were selected by VennDiagram and their enrichment in biological pathways was analyzed. Candidate gene biomarkers were selected using three different machine-learning algorithms (SVM-RFE, LASSO, RF).The diagnostic efficacy of these foundational genes was validated using independent datasets. Eventually, we validated molecular docking and medication prediction. We found 62 common DEGs, including several ones that could be enriched for Immune Response and Inflammation. Two DEGs (SELE and CCL20) could be identified by machine-learning algorithms. They performed well in diagnostic tests on independent datasets. In particular, we observed an upregulation of functions associated with the adaptive immune response, the leukocyte-lymphocyte-driven immunological response, and the proinflammatory response. Moreover, by ssGSEA, natural killer T cells, activated dendritic cells, activated CD4 T cells, neutrophils, and plasmacytoid dendritic cells were correlated with COVID-19 and PH, with SELE and CCL20 showing the strongest correlation with dendritic cells. Potential therapeutic compounds like FENRETI-NIDE, AFLATOXIN B1 and 1-nitropyrene were predicted. Further molecular docking and molecular dynamics simulations showed that 1-nitropyrene had the most stable binding with SELE and *** findings indicated that SELE and CCL20 were identified as novel diagnostic biomarkers for COVID-19 complicated with PH, and the target of these two key genes, FENRETI-NIDE and 1-nitropyrene, was predicted to be a potential therapeutic target, thus providing new insights into the prediction and treatment of COVID-19 compl

关键词： COVID-19 Pulmonary hypertension Differentially expressed genes machine algorithms Core gene Immune invasion Molecular docking

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Integrative analyses and validation of ferroptosis-related genes and mechanisms associated with cerebrovascular and cardiovascular ischemic diseases

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BMC GENOMICS 2023年第1期24卷 731-731页

作者： Liao, Wei Wen, Yuehui Zeng, Chuan Yang, Shaochun Duan, Yanyu He, Chunming Liu, Ziyou Gannan Med Univ Dept Neurosurg Affiliated 1 Ganzhou Jiangxi Peoples R China Gannan Med Univ Key Lab Prevent & Treatment Cardiovasc & Cerebrov Minist Educ Ganzhou Jiangxi Peoples R China Shanghai Jiao Tong Univ Sch Med Shanghai Peoples R China Gannan Med Univ Ganzhou Jiangxi Peoples R China Gannan Med Univ Heart Med Ctr Affiliated 1 Ganzhou Jiangxi Peoples R China Gannan Med Univ Dept Cardiac Surg Affiliated 1 Ganzhou Jiangxi Peoples R China

Background There has been a gradual increase in the occurrence of cardiovascular and cerebrovascular ischemic diseases, particularly as comorbidities. Yet, the mechanisms underlying these diseases remain unclear. Ferroptosis has emerged as a potential contributor to cardio-cerebral ischemic processes. Therefore, this study investigated the shared biological mechanisms between the two processes, as well as the role of ferroptosis genes in cardio-cerebral ischemic damage, by constructing co-expression modules for myocardial ischemia (MI) and ischemic stroke (IS) and a network of protein-protein interactions, mRNA-miRNA, mRNA-transcription factors (TFs), mRNA-RNA-binding proteins (RBPs), and mRNA-drug interactions. Results The study identified seven key genes, specifically ACSL1, TLR4, ADIPOR1, G0S2, PDK4, HP, PTGS2, and subjected them to functional enrichment analysis during ischemia. The predicted miRNAs were found to interact with 35 hub genes, and interactions were observed between 11 hub genes and 30 TF transcription factors. Additionally, 10 RBPs corresponding to 16 hub genes and 163 molecular compounds corresponding to 30 hub genes were identified. This study also clarified the levels of immune infiltration between MI and IS and different subtypes. Finally, we identified four hub genes, including TLR4, by using a diagnostic model constructed by Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis;ADIPOR1, G0S2, and HP were shown to have diagnostic value for the co-pathogenesis of MI and cerebral ischemia by both validation test data and RT-qPCR assay. Conclusions To the best our knowledge, this study is the first to utilize multiple algorithms to comprehensively analyze the biological processes of MI and IS from various perspectives. The four hub genes, TLR4, ADIPOR1, G0S2, and HP, have proven valuable in offering insights for the investigation of shared injury pathways in cardio-cerebral injuries. Therefore, these genes may serve as diagn

关键词： Ferroptosis Cerebrovascular ischemic diseases Cardiovascular ischemic diseases machine algorithms Common pathway of injury Diagnostic marker

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Constructing a Hospital Department Development-Level Assessment Model: machine Learning and Expert Consultation Approach in Complex Hospital Data Environments

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JMIR Formative Research 2024年 8卷 e54638页

作者： Liu, Jingkun Tai, Jiaojiao Han, Junying Zhang, Meng Li, Yang Yang, Hongjuan Yan, Ziqiang Big Data Analysis Center Honghui Hospital Xi'an Jiaotong University Xi'an China School of Foreign Studies Xi'an Medical University Xi'an China

Background: Every hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data. Objective: This study aims to use a novel machine learning algorithm to identify key evaluation indexes for hospital departments, offering insights for strategic planning and resource allocation in hospital management. Methods: Data related to the development of a hospital department over the past 3 years were extracted from various hospital information systems. The resulting data set was mined using neural machine algorithms to assess the possible role of hospital departments in the development of a hospital. A questionnaire was used to consult senior experts familiar with the hospital to assess the actual work in each hospital department and the impact of each department's development on overall hospital discipline. We used the results from this questionnaire to verify the accuracy of the departmental risk scores calculated by the machine learning algorithm. Results: Deep machine learning was performed and modeled on the hospital system training data set. The model successfully leveraged the hospital's training data set to learn, predict, and evaluate the working and development of hospital departments. A comparison of the questionnaire results with the risk ranking set from the departments machine learning algorithm using the cosine similarity algorithm and Pearson correlation analysis showed a good match. This indicates that the department development assessment model and risk score based on the objective data of hospital systems are relatively accurate and objective. Conclusions: This study demonstrated that our machine learning algorithm provides an accurate and objective assessment model for hospital department development. The strong alignment of the model's risk assessments with expert opinions, validated through statistical a

关键词： clustering hospital management machine algorithms model construction support vector machine

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Detection of Distinctions in Car Fleets Based on Measured and Simulated Data

Detection of Distinctions in Car Fleets Based on Measured an...

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Annual Reliability and Maintainability Symposium (RAMS)

作者： Hinz, Marcin Hienzsch, Fabian Bracke, Stefan Univ Wuppertal Chair Reliabil Engn & Risk Analyt Gaussstr 20 D-42119 Wuppertal Nrw Germany

ISBN: (纸本)9781509052844

The main aim of this paper is the analysis of a fleet behavior and comparison of damaged and non-damaged vehicles using different machine learning algorithms to identify the main distinctions in a car fleet. For this purpose OBD (On Board Diagnostics) signals are recorded in three various car types (BMW X3, Peugeot 208, Seat Leon) and several hundred drives. Furthermore, thousands of drives are simulated based on the Discrete Fourier Transformation (DTF) and Monte Carlo simulation (MC) to provide a huge training data for the rule learning algorithms. The simulated data represents the car fleet with given (or assumed) testing conditions. For example it is assumed, that all the cars of the fleet don't exceed the maximum speed of 170 km/hour due to the specs of the product. All made assumptions are variable and can be changed after the analysis process. All signals (simulated and recorded) are analyzed in order to provide information about every single drive (e.g. out of the speed signal: max speed, max acceleration, duration of the drive are determined and stored in a data base). [GRAPHICS] In this paper, data in form of matrixes with a dimension of more than 15.000 of (simulated) cars and couple of hundred variables is analyzed. The expected result is the rule/pattern for the cause of a damage of a vehicle. The found patterns (distinctive features), which distinguish the damaged and non-damaged vehicles are the basis information for the optimization of the test procedures of the next generation products. This approach provides a direct link between the analyzed field data out of the usage phase of a product and the test plans out of the development phase of the next generation product (cf. Fig. 1). The realization of the approach combined with the presented simulation algorithms provides a very powerful tool for the reliability engineers across all technical disciplines. The results of the analysis obtained from machine learning algorithms are in fact not easy to int

关键词： Testing optimization of test procedures field data analysis machine algorithms On Board Diagnostics

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Seeing faces in noise: Exploring machine and human face detection processes by the reverse correlation method

Seeing faces in noise: Exploring machine and human face dete...

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Annual Summit and Conference of Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA)

作者： Saegusa, Chihiro Yamaoka, Megumi Watanabe, Katsumi Univ Tokyo Res Ctr Adv Sci & Technol Tokyo Japan Panasonic Corp Osaka Japan

ISBN: (纸本)9786163618238

In the present study, we aimed at investigating possible similarities (and discrepancies) between two major machine algorithms of face detection (AdaBoost and EigenFace) and human face detection processes. For this, we presented the "false classification images" produced by the two face detection algorithms to human observers. Noise fields were fed into the two algorithms and images in which each algorithm falsely detected faces were collected. Those images were averaged and normalized to obtain false classification images. Human observers performed a psychophysical experiment to detect a face with the false classification images against random noise images. The face detection performance increased almost linearly as the number of averaged false detection images increase. Inverted images reduced the detection performance more with the images produced by EigenFace than those by AdaBoost. The present results suggest that both human and machine detection algorithms tended to make similar errors and therefore both AdaBoost and EigenFace are good approximation of human face processing.

关键词： correlation methods face recognition image classification image denoising learning (artificial intelligence) AdaBoost EigenFace false classification images human face detection process machine algorithms psychophysical experiment reverse correlation method Classification algorithms Decision support systems Face Face detection Noise Observers Pattern recognition

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