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Machine learning analysis of gene expression profile reveals a novel diagnostic signature for osteoporosis

JOURNAL OF ORTHOPAEDIC SURGERY AND RESEARCH 2021年第1期16卷 189-189页

作者： Chen, Xinlei Liu, Guangping Wang, Shuxiang Zhang, Haiyang Xue, Peng Zibo Cent Hosp Dept Orthoped Zibo 255000 Shandong Peoples R China

BackgroundOsteoporosis (OP) is increasingly prevalent with the aging of the world population. It is urgent to identify efficient diagnostic signatures for the clinical *** downloaded the mRNA profile of 90 peripheral blood samples with or without OP from GEO database (Number: GSE152073). Weighted gene co-expression network analysis (WGCNA) was used to reveal the correlation among genes in all samples. GO term and KEGG pathway enrichment analysis was performed via the clusterProfiler R package. STRING database was applied to screen the interaction pairs among proteins. Protein-protein interaction (PPI) network was visualized based on Cytoscape, and the key genes were screened using the cytoHubba plug-in. The diagnostic model based on these key genes was constructed, and 5-fold cross validation method was applied to evaluate its *** gene module consisted of 176 genes predicted to be associated with the occurrence of OP was identified. A total of 16 significantly enriched GO terms and 1 significantly enriched KEGG pathway were obtained based on the 176 genes. The top 50 key genes in the PPI network were identified. Then 22 genes were screened based on stepwise regression analysis from the 50 key genes. Of which, 9 genes were further screened out by multivariate regression analysis with the significant threshold of P value < 0.01. The diagnostic model was established based on the optimal 9 key genes, which efficiently separated the normal samples and OP *** diagnostic model established based on nine key genes could reliably separate OP patients from healthy subjects, which provided novel lightings on the diagnostic research of OP.

关键词： Osteoporosis WGCNA analysis PPI network logistic regression model

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Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis

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BMC SURGERY 2023年第1期23卷 267-267页

作者： Liu, Fei Yao, Jie Liu, Chunyan Shou, Songtao Tianjin Med Univ Gen Hosp Dept Emergency Med 154 Anshan Rd Tianjin 300052 Peoples R China Hebei North Univ Dept Anesthesiol Affiliated Hosp 1 Zhangjiakou 075000 Hebei Peoples R China Hebei North Univ Dept Intens Care Unit Affiliated Hosp 1 Zhangjiakou 075000 Hebei Peoples R China

BackgroundThis study aimed to construct predictive models for the risk of sepsis in patients with Acute pancreatitis (AP) using machine learning methods and compared optimal one with the logistic regression (LR) model and scoring *** this retrospective cohort study, data were collected from the Medical Information Mart for Intensive Care III (MIMIC III) database between 2001 and 2012 and the MIMIC IV database between 2008 and 2019. Patients were randomly divided into training and test sets (8:2). The least absolute shrinkage and selection operator (LASSO) regression plus 5-fold cross-validation were used to screen and confirm the predictive factors. Based on the selected predictive factors, 6 machine learning models were constructed, including support vector machine (SVM), K-nearest neighbour (KNN), multi-layer perceptron (MLP), LR, gradient boosting decision tree (GBDT) and adaptive enhancement algorithm (AdaBoost). The models and scoring systems were evaluated and compared using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and the area under the curve (AUC).ResultsA total of 1, 672 patients were eligible for participation. In the training set, 261 AP patients (19.51%) were diagnosed with sepsis. The predictive factors for the risk of sepsis in AP patients included age, insurance, vasopressors, mechanical ventilation, Glasgow Coma Scale (GCS), heart rate, respiratory rate, temperature, SpO2, platelet, red blood cell distribution width (RDW), International Normalized Ratio (INR), and blood urea nitrogen (BUN). The AUC of the GBDT model for sepsis prediction in the AP patients in the testing set was 0.985. The GBDT model showed better performance in sepsis prediction than the LR, systemic inflammatory response syndrome (SIRS) score, bedside index for severity in acute pancreatitis (BISAP) score, sequential organ failure assessment (SOFA) score, quick-SOFA (qSOFA), and simplified acute physiology s

关键词： Machine learning models logistic regression model Scoring systems sepsis Acute pancreatitis

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Diagnosis of Compressed Nerve Root in Lumbar Disc Herniation Patients by Surface Electromyography

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ORTHOPAEDIC SURGERY 2018年第1期10卷 47-55页

作者： Li, Wei Liu, Yan-cheng Zheng, Chen-fan Miao, Jun Chen, Hui Quan, Hai-ying Yan, Song-hua Zhang, Kuan Capital Med Univ Sch Biomed Engn 10 Xitoutiao Beijing 100069 Peoples R China Capital Med Univ Beijing Key Lab Fundamental Res Biomech Clin Appl Beijing Peoples R China Tianjin Hosp Dept Spinal Surg Tianjin Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Suzhou Peoples R China

Objective: To establish a logistic regression model using surface electromyography (SEMG) parameters for diagnosing the compressed nerve root at L5 or S1 level in patients with lumbar disc herniation (LDH). Methods: This study recruited 24 patients with L5 nerve root compression and 23 patients with S1 nerve root compression caused by LDH from May 2014 to May 2016. SEMG signals from the bilateral tibialis anterior and lateral gastrocnemius were measured. The root mean square (RMS), the RMS peak time, the mean power frequency (MPF), and the median frequency (MF) were analyzed. The accuracy, sensitivity, and specificity values were calculated separately. The areas under the curve (AUC) of the receiver-operating characteristic (ROC) curve and the kappa value were used to evaluate the accuracy of the SEMG diagnostic model. Results: The accuracy of the SEMG model ranged from 85.71% to 100%, with an average of 93.57%. The sensitivity, specificity, AUC, and kappa value of the logistic regression model were 0.98 +/- 0.05, 0.92 +/- 0.09, 0.95 +/- 0.04 (P = 0.006), and 0.87 +/- 0.11, respectively (P = 0.001). The final diagnostic model was: P = 1-1 1+ ey;y = 10.76 (5.95 x TA_RMS Ratio) -(0.38 x TA_RMS Peak Time Ratio) -(5.44 x 44 x LG_RMS Peak Time Ratio). L5 nerve root compression is diagnosed when P < 0.5 and S-1 nerve root compression when P = 0.5. Conclusions: The logistic regression model developed in this study showed high diagnostic accuracy in detecting the compressed nerve root (L5 and S1) in these patients with LDH.

关键词： Compressed nerve root Diagnosis logistic regression model Lumbar disc herniation Surface electromyography

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Prediction of corporate financial distress based on digital signal processing and multiple regression analysis

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第1期8卷 2209-2220页

作者： Li, Liyang Yousif, Mohammed El-Kanj, Nasser Shijiazhuang Univ Appl Technol Dept Econ & Trade Shijiazhuang 050000 Hebei Peoples R China Appl Sci Univ Coll Adm Sci Al Eker Bahrain Amer Univ Middle East Coll Business Adm Egaila Al Ahmadi Kuwait

In order to reduce the default rate of corporate bond market, the author proposes to use digital signal processing and multiple regression analysis to study the prediction system of financial distressed companies. First, design the research method, logistic regression model is the most commonly used multivariate statistical method when modeling binary dependent variables, it can solve the problem of nonlinear classification, it has no specific requirements for the distribution of variables, and the accuracy of judgment is high. The author selects 32 financial ratios from the perspectives of solvency, operating ability, profitability, development ability, per share index, and risk level. Taking special treatment (ST) due to abnormal financial status as a sign of financial distress in listed companies, when selecting samples, the matching principle is adopted to select non-ST companies as matching samples. Two methods of logistic regression and support vector machine are used for empirical testing, and both in-sample testing and out-of-sample prediction are performed. The results show that when using the logistic regression method, the propensity to default indicator (TTD) reflected in the text content, it can indeed improve the out-of-sample prediction accuracy of the financial distress prediction model, and it is consistent with the in-sample test, this is mainly reflected in the reduction of the first type of error, that is, the probability of misjudging a financially distressed company as a normal company. Changes in the proportions have little effect on the relative importance of financial ratio variables when modeling with support vector machines, the propensity to default indicator (TTD) entered the top ten important variables in both ratios, and ranked fourth among all indicators when the ratio was 1:2, importance has increased significantly. From this it can be seen that, when using support vector machine to build a financial distress prediction model, the pr

关键词： Digital signal multiple regression corporate financial distress logistic regression model support vector machine

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CD10 and Das1: a biomarker study using immunohistochemistry to subtype gastric intestinal metaplasia

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BMC GASTROENTEROLOGY 2022年第1期22卷 197-197页

作者： Koulis, Athanasios Di Costanzo, Natasha Mitchell, Catherine Lade, Stephen Goode, David Busuttil, Rita A. Boussioutas, Alex Peter MacCallum Canc Ctr Upper Gastrointestinal Translat Res Lab Melbourne Vic Australia Univ Melbourne Sir Peter MacCallum Dept Oncol Melbourne Vic Australia Peter MacCallum Canc Ctr Dept Pathol Melbourne Vic Australia Peter MacCallum Canc Ctr Computat Canc Biol Program Melbourne Vic Australia Royal Melbourne Hosp Dept Med Melbourne Vic Australia Peter MacCallum Canc Ctr Upper Gastrointestinal Translat Res Lab 305 Grattan St Parkville Vic 3050 Australia

Background Intestinal metaplasia (IM) is considered a key pivot point in the Correa model of gastric cancer (GC). It is histologically subtyped into the complete and incomplete subtypes, the latter being associated with a greater risk of progression. However, the clinical utility of IM subtyping remains unclear, partially due to the absence of reliable defining biomarkers. Methods Based on gene expression data and existing literature, we selected CD10 and Das1 as candidate biomarkers to distinguish complete and incomplete IM glands in tissues from patients without GC (IM-GC) and patients with GC (IM + GC). Immunohistochemical staining of individually subtyped IM glands was scored after blinding by two researchers using tissue belonging to both IM-GC and IM + GC patients. Whole tissue Das1 staining was further assessed using digital image quantification (cellSens Dimension, Olympus). Results Across both cohorts CD10 stained the IM brush border and was shown to have a high sensitivity (87.5% and 94.9% in IM-GC and IM + GC patients respectively) and specificity (100.0% and 96.7% respectively) with an overall AUROC of 0.944 for complete IM glands. By contrast Das1 stained mainly goblet cells and the apical membrane of epithelial cells, mostly of incomplete IM glands with a low sensitivity (28.6% and 29.3% in IM-GC and IM + GC patients respectively) but high specificity (98.3% and 85.1% respectively) and an overall AUROC of 0.603 for incomplete IM glands. A combined logistic regression model showed a significant increase in AUROC for detecting complete IM glands (0.955 vs 0.970). Whole tissue digital quantification of Das1 staining showed a significant association with incomplete IM compared to complete IM, both in IM-GC and in IM + GC patients (p = 0.016 and p = 0.009 respectively, Mann-Whitney test and unpaired t test used). Additionally, complete IM in IM + GC patients exhibited significantly more Das1 staining than in IM-GC patients (p = 0.019, Mann-Whitney test). Co

关键词： Intestinal metaplasia subtypes Risk of progression Gastric cancer Gene expression profiling Biomarkers CD10 Das1 Immunohistochemistry logistic regression model Digital quantification

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Diagnostic signature composed of seven genes in HIF-1 signaling pathway for preeclampsia

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BMC PREGNANCY AND CHILDBIRTH 2023年第1期23卷 233-233页

作者： Yang, Xun Yu, Ling Ding, Yiling Yang, Mengyuan Cent South Univ Xiangya Hosp 2 Dept Obstet & Gynecol Changsha 410011 Hunan Peoples R China

PurposeIn this study, we explored the relationship of genes in HIF-1 signaling pathway with preeclampsia and establish a logistic regression model for diagnose preeclampsia using bioinformatics *** microarray datasets GSE75010 and GSE35574 were downloaded from the Gene Expression Omnibus database, which was using for differential expression analysis. DEGs were performed the Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene set enrichment analysis (GSEA). Then we performed unsupervised consensus clustering analysis using genes in HIF-1 signaling pathway, and clinical features and immune cell infiltration were compared between these clusters, as well as the least absolute shrinkage and selection operator (LASSO) method to screened out key genes to constructed logistic regression model, and receiver operating characteristic (ROC) curve was plotted to evaluate the accuracy of the ***57 DEGs were identified, of which GO, KEGG and analysis GSEA showed DEGs were mostly involved in HIF-1 signaling pathway. Two subtypes were identified of preeclampsia and 7 genes in HIF1-signaling pathway were screened out to establish the logistic regression model for discrimination preeclampsia from controls, of which the AUC are 0.923 and 0.845 in training and validation datasets *** genes (including MKNK1, ARNT, FLT1, SERPINE1, ENO3, LDHA, BCL2) were screen out to build potential diagnostic model of preeclampsia.

关键词： Placenta Microarray Integrated analysis Consensus clustering logistic regression model Immune cell infiltration

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Parsimonious clinical prediction model for the diagnosis of complicated appendicitis

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HELIYON 2023年第8期9卷 e19067页

作者： Cai, Jia-hui Zhou, Hui Liang, Dan Chen, Qiao Xiao, Yeyu Li, Guang-ming Guangzhou Hosp Integrated Tradit & West Med Dept Radiol Yingbin Ave 87 Guangzhou 510800 Guangdong Peoples R China Qingyuan Peoples Hosp Dept Radiol Yinquan Rd B24 Qingyuan 511500 Guangdong Peoples R China Guangzhou First Peoples Hosp Dept Radiol Panfu Rd 1 Guangzhou Guangdong Peoples R China

Objective: To develop a logistic regression model that combines clinical and radiological parameters for prediction of complicated appendicitis. Methods: 248 patients with histologically proven uncomplicated (n = 214) and complicated (n = 34) acute appendicitis were analyzed retrospectively. All patients had undergone a presurgical abdominal and/or pelvic computed tomography (CT) scan, assessed by two radiologists. A model using univariate and multivariate logistic regression analyses was developed, and the strength of association between independent predictors and complicated acute appendicitis was evaluated by adjusted odds radio. Clinical parameters were gender, age, anorexia, vomiting, duration of symptoms, right lower abdominal quadrant (RLQ) tenderness, rebound tenderness, body temperature, white blood cell (WBC) count, and neutrophil ratio. Radiological parameters were ap-pendix diameter, appendicolith, caecal wall thickening, mesenteric lymphadenopathy, extraluminal air, abscess, fat stranding, and periappendicular fluid. Results: Four features (body temperature>37.2 degrees C, vomiting, appendicolith, and periappendiceal fluid) were included in the logistic regression model, and yielded an area under the curve (AUC) of 0.87 (95% confidence interval (CI), 0.80-0.93), sensitive of 88%, and specificity of 74%. Conclusion: The logistic regression model makes an accurate and simple prediction of complicated appendicitis possible.

关键词： Complicated appendicitis Predictive factors logistic regression model Computed tomography Abdominal imaging

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Applications of regression models in Epidemiology 1

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2017年

作者： Erick Suárez Cynthia M. Pérez Roberto Rivera Melissa N. Martínez

ISBN: (数字)9781119212515

ISBN: (纸本)9781119212485

A one-stop guide for public health students and practitioners learning the applications of classical regression models in epidemiology This book is written for public health professionals and students interested in applying regression models in the field of epidemiology. The academic material is usually covered in public health courses including (i) Applied regression Analysis, (ii) Advanced Epidemiology, and (iii) Statistical Computing. The book is composed of 13 chapters, including an introduction chapter that covers basic concepts of statistics and probability. Among the topics covered are linear regression model, polynomial regression model, weighted least squares, methods for selecting the best regression equation, and generalized linear models and their applications to different epidemiological study designs. An example is provided in each chapter that applies the theoretical aspects presented in that chapter. In addition, exercises are included and the final chapter is devoted to the solutions of these academic exercises with answers in all of the major statistical software packages, including STATA, SAS, SPSS, and R. It is assumed that readers of this book have a basic course in biostatistics, epidemiology, and introductory calculus. The book will be of interest to anyone looking to understand the statistical fundamentals to support quantitative research in public health. In addition, this book: • Is based on the authors’ course notes from 20 years teaching regression modeling in public health courses • Provides exercises at the end of each chapter • Contains a solutions chapter with answers in STATA, SAS, SPSS, and R • Provides real-world public health applications of the theoretical aspects contained in the chapters Applications of regression models in Epidemiology is a reference for graduate students in public health and public health practitioners. ERICK SUÁREZ is a Professor of the Department of Biostatistics and Epidemiology at the University of Puerto

关键词： regression analysis statistical methods public health Applied regression Analysis Advanced Epidemiology Statistical Computing for Applying Statistical modeling linear regression model polynomial regression model weighted linear regression logistic regression model Poisson regression model STATA SAS SPSS R

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modeling and Analysis of Landslide Potential for Alishan Mountain Road in Southwestern Taiwan

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Journal of Chinese Soil and Water Conservation 2020年第2期51卷 55-64页

作者： Huang, Yen-Ning Yeh, Chao-Hsien Department of Power Development Taipower Power Company Taipei 100 Taiwan Department of Water Resources Enginnering and Conservation Feng Chia University Taichang 407 Taiwan

In this study, landslide potential models for Alishan Mountain Road in southwestern Taiwan were developed and assessed. We collected data on severe typhoon events that occurred in Taiwan between 2007 and 2011 and considered seven intrinsic and extrinsic factors. The dangerous value method and logistic regression were used to build the mountain road landslide potential models, which were calibrated using typhoon event data. The results indicated that cumulative precipitation and the geological characteristics of the stratum were the most important factors in the models. The logistic regression model generated estimations of historical disaster locations with 96.76% accuracy. The calibrated landslide potential model was used to analyze and predict the distribution of areas with higher landslide potential on Alishan Mountain Road under heavy rain and extremely heavy rain conditions. © 2020, Chinese Soil and Water Conservation Society. All right reserved.

关键词： Dangerous value method Landslide potential model logistic regression model

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A 2 miRNAs-based signature for the diagnosis of atherosclerosis

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BMC CARDIOVASCULAR DISORDERS 2021年第1期21卷 150-150页

作者： Han, Xiujiang Wang, Huimin Li, Yongjian Liu, Lina Gao, Sheng Tianjin NanKai Hosp Dept Geriatr 6 Changjiang Rd Tianjin 300100 Peoples R China Tianjin NanKai Hosp Dept Neurol Tianjin 300100 Peoples R China Tianjin NanKai Hosp Dept Cardiovasc Med 1 Tianjin 300100 Peoples R China Nankai Univ 94 Weijin Rd Tianjin 300071 Peoples R China

Background Atherosclerosis (AS) is a leading cause of vascular disease worldwide. MicroRNAs (miRNAs) play an essential role in the development of AS. However, the miRNAs-based biomarkers for the diagnosis of AS are still limited. Here, we aimed to identify the miRNAs significantly related to AS and construct the predicting model based on these miRNAs for distinguishing the AS patients from healthy cases. Methods The miRNA and mRNA expression microarray data of blood samples from patients with AS and healthy cases were obtained from the GSE59421 and GSE20129 of Gene Expression Omnibus (GEO) database, respectively. Weighted Gene Co-expression Network Analysis (WGCNA) was performed to evaluate the correlation of the miRNAs and mRNAs with AS and identify the miRNAs and mRNAs significantly associated with AS. The potentially critical miRNAs were further optimized by functional enrichment analysis. The logistic regression models were constructed based on these optimized miRNAs and validated by threefold cross-validation method. Results WGCNA revealed 42 miRNAs and 532 genes significantly correlated with AS. Functional enrichment analysis identified 12 crucial miRNAs in patients with AS. Moreover, 6 miRNAs among the identified 12 miRNAs, were selected using a stepwise regression model, in which four miRNAs, including hsa-miR-654-5p, hsa-miR-409-3p, hsa-miR-485-5p and hsa-miR-654-3p, were further identified through multivariate regression analysis. The threefold cross-validation method showed that the AUC of logistic regression model based on the four miRNAs was 0.7308, 0.8258, and 0.7483, respectively, with an average AUC of 0.7683. Conclusion We identified a total of four miRNAs, including hsa-miR-654-5p and hsa-miR-409-3p, are identified as the potentially critical biomarkers for AS. The logistic regression model based on the identified 2 miRNAs could reliably distinguish the patients with AS from normal cases.

关键词： Atherosclerosis Diagnosis miRNAs Signature WGCNA logistic regression model

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