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Identification and verification of diagnostic biomarkers for deep infiltrating endometriosis based on machine learning algorithms

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JOURNAL OF BIOLOGICAL ENGINEERING 2024年第1期18卷 1-17页

作者： Shi, Shanping Huang, Chao Tang, Xiaojian Liu, Hua Feng, Weiwei Chen, Chen Shanghai Jiao Tong Univ Ruijin Hosp Sch Med Dept Obstet & Gynecol Shanghai 200025 Peoples R China

This study addresses the challenges in the early diagnosis of deep infiltrating endometriosis (DIE) by exploring the potential role of the deubiquitinating enzyme USP14. By analyzing the GSE141549 dataset from the Gene Expression Omnibus (GEO) database, using bioinformatics methods and three machine learning algorithms (LASSO, Random Forest, and Support Vector machine), the key feature gene USP14 was identified. The results indicated that USP14 is significantly upregulated in DIE and exhibits good predictive value (AUC = 0.786). Further analysis revealed the important role of USP14 in muscle function, cellular growth factor response, and maintenance of chromosome structure, and its close association with various immune cell functions. Immunohistochemical staining confirmed the high expression of USP14 in DIE tissues. This study provides a new molecular target for the early diagnosis of DIE, which holds significant clinical implications and potential application value.

关键词： Deep infiltrating endometriosis (DIE) USP14 machine learning algorithms Single-cell RNA sequencing (scRNA-seq) Immunohistochemical staining

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Prediction models based on machine learning algorithms for COVID-19 severity risk

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BMC PUBLIC HEALTH 2025年第1期25卷 1-19页

作者： Zhang, Hansong Wang, Ying Xie, Yan Wang, Cuihan Ma, Yuqi Jin, Xin Tianjin Univ Sch Mech Engn Tianjin 300350 Peoples R China Tianjin First Ctr Hosp Dept Nursing Tianjin 300196 Peoples R China Tianjin First Ctr Hosp Dept Liver Transplantat Tianjin 300196 Peoples R China Tianjin Med Univ Tianjin Nankai Hosp Tianjin 300000 Peoples R China Tianjin Key Lab Acute Abdomen Dis Associated Organ Tianjin 300000 Peoples R China Inst Integrat Med Acute Abdominal Dis Tianjin 300000 Peoples R China Tianjin Univ Med Sch Tianjin 300072 Peoples R China Tianjin Municipal Hlth Commiss Tianjin 300070 Peoples R China

BackgroundThe World Health Organization has highlighted the risk of Disease X, urging pandemic preparedness. Coronavirus disease 2019 (COVID-19) could be the first Disease X;therefore, understanding the epidemiological experiences of COVID-19 is crucial while preparing for future similar *** models for COVID-19 severity risk in hospitalized patients were constructed based on four machine learning algorithms, namely, logistic regression, Cox regression, support vector machine (SVM), and random forest. These models were evaluated for prediction accuracy, area under the curve (AUC), sensitivity, and specificity as well as were interpreted using SHapley Additive *** were collected from 1,485 hospitalized patients across 6 centers, comprising 1,184 patients with severe or critical COVID-19 and 301 patients with nonsevere COVID-19. Among the four models, the SVM model achieved the highest prediction accuracy of 98.45%, with an AUC of 0.994, a sensitivity of 0.989, and a specificity of 0.969. Moreover, oxygenation index (OI), confusion, respiratory rate, and age were found to be predictors of COVID-19 severity *** could accurately predict COVID-19 severity risk;thus, it can be prioritized as a prediction model. OI is the most critical predictor of COVID-19 severity risk and can serve as the primary and independent evaluation indicator.

关键词： Prediction models machine learning algorithms Severity risk COVID-19

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Prediction of air compressor condition using vibration signals and machine learning algorithms

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JOURNAL OF VIBRATION AND CONTROL 2023年第5-6期29卷 1342-1351页

作者： Aravinth, S. Sugumaran, V Vellore Inst Technol Sch Mech Engn Chennai CampusVandalur Kelambakkam Rd Chennai 600127 Tamil Nadu India

Air compressors are widely used equipment in the modern world for their tremendous utilization in applications of both domestic and industrial sectors. The inbuilt mechanical parts are often prone to various failures due to the complexity in the construction of air compressors that affects the overall system process. Hence, it is essential to devise a methodology to identify the failures at the early stages of its operation to avoid the major causalities due to process breakdown and system seizure. In this study, a single-acting single-stage reciprocating air compressor was chosen. The fault conditions like inlet valve fluttering, outlet valve fluttering, valve plate leakage, and check valve fault were considered. The statistical, histogram and autoregressive moving average features were extracted from the raw vibration signals. The most dominating features were selected using a decision tree algorithm and those features were classified using machine learning classifiers like Lazy K Star, Decorate, and radial basis function networks. The classifier Lazy K Star on autoregressive moving average feature exhibits the highest fault classification rate of 99.67% in classifying various compressor conditions and the results were compared and presented.

关键词： Air compressor condition monitoring fault diagnosis machine learning algorithms vibration signals

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Modeling potential wetland distributions in China based on geographic big data and machine learning algorithms

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International Journal of Digital Earth 2023年第1期16卷 3706-3724页

作者： Hengxing Xiang Yanbiao Xi Dehua Mao Tianyuan Xu Ming Wang Fudong Yu Kaidong Feng Zongming Wang State Key Laboratory of Black Soils Conservation and Utilization Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchunPeople’s Republic of China International Institute for Earth System Science Nanjing UniversityNanjingPeople’s Republic of China School of Foreign Languages Zhongnan University of Economics and LawWuhanPeople’s Republic of China College of Information and Technology Jilin Agricultural UniversityChangchunPeople’s Republic of China National Earth System Science Data Center BeijingPeople’s Republic of China

Climate change and human activities have reduced the area and degraded the functions and services of wetlands in *** protect and restore wetlands,it is urgent to predict the spatial distribution of potential *** this study,the distribution of potential wetlands in China was simulated by integrating the advantages of Google Earth Engine with geographic big data and machine learning *** on a potential wetland database with 46,000 samples and an indicator system of 30 hydrologic,soil,vegetation,and topographic factors,a simulation model was constructed by machine learning *** accuracy of the random forest model for simulating the distribution of potential wetlands in China was good,with an area under the receiver operating characteristic curve value of *** area of potential wetlands was 332,702 km^(2),with 39.0%of potential wetlands in Northeast *** features were notable,and potential wetlands were mainly concentrated in areas with 400-600 mm precipitation,semi-hydric and hydric soils,meadow and marsh vegetation,altitude less than 700 m,and slope less than 3°.The results provide an important reference for wetland remote sensing mapping and a scientific basis for wetland management in China.

关键词： Potential wetland distribution machine learning algorithms geographic big data China wetland geographic features

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RA-ECDM of Silicon Wafers Using Taguchi's Methodology and machine learning algorithms

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SILICON 2023年第3期15卷 1511-1526页

作者： Singh, Manpreet Antil, Parvesh Singh, Sarbjit Katal, Nitish Bakshi, Dapinder Kaur Alkesh Punjab State Council Sci & Technol Chandigarh India CCS HAU Coll Agr Engn & Technol Dept Basic Engn Hisar Haryana India Punjab Engn Coll Dept Mech Engn Chandigarh India Indian Inst Informat Technol Sch Elect Una Himachal Prades India

As a prominent machining process, electrochemical discharge machining (ECDM) is used to process materials that are both fragile and difficult to cut. The growing use of this method for the machining of hard to cut materials has paved the way for the discovery of unexploited potential in machining outcomes. In the current study, silicon wafers were machined by rotary assisted electrochemical discharge machining using Taguchi's L-27 orthogonal array, having output process characteristics such as overcut and hole circularity. The optimization through Taguchi's methodology shows that the overcut and circularity of holes improved by 1.40% and 2.44% by using optimal parametric combination as compared to orthogonal array. All output process parameters were again analysed through a machine learning algorithm by determining their root mean squared error (RMSE), R-Squared, mean squared error (MSE), mean absolute error (MAE), prediction speed, and training time. The obtained results show that in regression models of the Gaussian process, overcut and circularity of hole are well predicted by both the exponential and squared exponential regression models. The overcut and circularity of hole models had R-squared values of 0.92 and 0.90, respectively.

关键词： machine learning algorithms RA-ECDM Silicon Wafers Taguchi's Methodology

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Multi-class classification of COVID-19 documents using machine learning algorithms

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2023年第2期60卷 571-591页

作者： Rabby, Gollam Berka, Petr Prague Univ Econom & Business Dept Informat & Knowledge Engn Prague Czech Republic

In most biomedical research paper corpus, document classification is a crucial task. Even due to the global epidemic, it is a crucial task for researchers across a variety of fields to figure out the relevant scientific research papers accurately and quickly from a flood of biomedical research papers. It can also assist learners or researchers in assigning a research paper to an appropriate category and also help to find the relevant research paper within a very short time. A biomedical document classifier needs to be designed differently to go beyond a "general" text classifier because it's not dependent only on the text itself (i.e. on titles and abstracts) but can also utilize other information like entities extracted using some medical taxonomies or bibliometric data. The main objective of this research was to find out the type of information or features and representation method creates influence the biomedical document classification task. For this reason, we run several experiments on conventional text classification methods with different kinds of features extracted from the titles, abstracts, and bibliometric data. These procedures include data cleaning, feature engineering, and multi-class classification. Eleven different variants of input data tables were created and analyzed using ten machine learning algorithms. We also evaluate the data efficiency and interpretability of these models as essential features of any biomedical research paper classification system for handling specifically the COVID-19 related health crisis. Our major findings are that TF-IDF representations outperform the entity extraction methods and the abstract itself provides sufficient information for correct classification. Out of the used machine learning algorithms, the best performance over various forms of document representation was achieved by Random Forest and Neural Network (BERT). Our results lead to a concrete guideline for practitioners on biomedical document classificatio

关键词： Multi-class classification machine learning algorithms Text mining COVID-19

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A review and guide on selecting and optimizing machine learning algorithms for daylight prediction

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BUILDING AND ENVIRONMENT 2023年 244卷

作者： Liu, Qiuping Chen, Yaodong Liu, Yang Lei, Yuanfang Wang, Yibo Hu, Pantin Southwest Jiaotong Univ Sch Architecture Chengdu Peoples R China SWJTU Southwest Jiaotong Univ Chengdu Sichuan Peoples R China

Daylight confers extensive benefits for building occupants and improves energy efficiency;thus, its prediction and performance are significant for design decision-making on building and lighting. However, real-time prediction with high precision is difficult since daylight is spatiotemporal correlated and highly dynamic (e.g., periodic, irregularity). In recent years, numerous studies have been focused on machine learning algorithms due to their high generalization ability, robustness, and accuracy in dealing with complex problems. In contrast, selecting the optimal algorithms for a specific research problem is time-consuming and challenging, especially for new learners. This review attempts to develop a systematic guide for algorithms selection and optimization by: (i) discussing the main principles, model structure, and characteristics of 36 of the most representative algorithms;(ii) reviewing and conducting a whole process statistical analysis on the machine learning-based daylight-prediction studies, while abstracting the statistical issues (regression and classification) and prediction tasks (time series, spatial, and spatiotemporal predictions) of different research topics and purposes;and (iii) integrating the findings, developing the selection and optimization methodologies for algorithms. In conclusion, the order of specific machine learning algorithms is highly recommended for different problem types: (i) for regression, Back Propagation Neural Network (recommendation level = 0.82) > Convolutional Neural Network (0.78) > Kernel Support Vector Regression (0.72);(ii) for classification, Kernel Support Vector machine (0.8) > Radial Basis Function Neural Network (0.56) > Random Forest (0.53);(iii) for time series prediction, Back Propagation Neural Network (0.66) > Random Forest (0.61) > Gated Recurrent Unit Network (0.52) = EXtreme Gradient Boosting (0.52);(iv) for spatial prediction, Back Propagation Neural Network (0.82) > Convolutional Neural Network (0.78

关键词： Daylight prediction machine learning algorithms Time-series prediction Spatial prediction Artificial neural network

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Influence of elevation on plastic-covered greenhouse mapping accuracy using high-resolution satellite imagery and machine learning algorithms

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REMOTE SENSING LETTERS 2023年第4期14卷 413-422页

作者： Veettil, Bijeesh Kozhikkodan Acharki, Siham Van Lang Univ Sci & Technol Adv Inst Lab Ecol & Environm Management Ho Chi Minh City Vietnam Van Lang Univ Fac Appl Technol Sch Technol Ho Chi Minh City Vietnam Abdelmalek Essaadi Univ Fac Sci & Tech Tangier Dept Earth Sci Tetouan Morocco

Proliferation of plastic-covered greenhouse (PCG) farming areas has been unparalleled in recent decades around the globe. Therefore, PCG's proper mapping and continuous monitoring are required to maintain protected farming sustainability and to reduce plastic pollution. Topography is one of the key factors affecting PCG mapping accuracy from satellite imagery. In this research, in order to understand the influence of elevation on PCG mapping accuracy in a Moroccan (with altitude below 200 m) and Vietnamese (an average altitude of 1,500 m) area, five supervised algorithms were applied to high-resolution PlanetScope and RapidEye imagery: maximum likelihood classification (MLC), k-nearest neighbours (k-NN), random forest (RF), support vector machine with linear kernel (SVML) and radial basis kernel (SVMRB). The findings indicated that k-NN (SVMRB) produced the greatest overall accuracy, followed by SVMRB (RF) for Dalat City (Loukkos). The present study also found that RapidEye outperforms PlanetScope in both areas and independently of the machine learning algorithm. Elevation data inclusion significantly improves (1.6% to 6.8%) various satellites' performance, particularly in Dalat City. Thus, this study demonstrated that topography has an impact on PCG mapping, especially in hilly areas.

关键词： High-resolution imagery topography plastic-covered greenhouse protected horticulture machine learning algorithms

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Magnitude Type Conversion Models for Earthquakes in Turkey and Its Vicinity with machine learning algorithms

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JOURNAL OF EARTHQUAKE ENGINEERING 2023年第9期27卷 2533-2554页

作者： Coban, Kaan Hakan Sayil, Nilgun Karadeniz Tech Univ Engn Fac Dept Geophys Engn Trabzon Turkey

In this study, three new regression models are created for magnitude-type conversion with different machine learning algorithms (linear regression, regression trees, support vector machines, Gaussian process regression models, ensembles of trees) by using the earthquakes (M >= 4.0) that occurred in Turkey (1900-2020). Additionally, eight new equations are formed with linear and orthogonal regression methods. Developed equations and models are compared to equations selected from the literature by test data. As a result of the study, it is observed that machine learning algorithms create better models and provide results closer to the real values than created and selected equations.

关键词： machine learning algorithms regression trees support vector machines magnitude Regression

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A Smart Gas Sensor Using machine learning algorithms: Sensor Types Based on IED Configurations, Fabrication Techniques, Algorithmic Approaches, Challenges, Progress, and Limitations: A Review

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IEEE SENSORS JOURNAL 2023年第11期23卷 11336-11355页

作者： Nasri, Abdelghaffar Boujnah, Aicha Boubaker, Aimen Kalboussi, Adel Icam F-31300 Toulouse France Univ Monastir Dept Phys Fac Sci Monastir 5000 Tunisia

Over the past decade, machine learning (ML) and artificial intelligence (AI) have attracted great interest in research and various practical applications. Currently, smart, fast, and high sensitivity with excellent selectivity are becoming increasingly interesting due to the high need for environmental safety and medical applications. The main challenge is to improve sensor selectivity, which requires the combination of interdisciplinary research areas to successfully develop smart gas/chemical sensing devices with better performance. In this review, we present a few principles of gas sensing based on low-cost interdigital electrodes (IDEs), such as electrochemical, resistive, capacitive, and acoustic sensors. In addition, the most important current methods for improving gas sensing performance, the different materials, the different techniques used to fabricate IDE gas sensors, and their advantages and limitations are presented. In addition, a comparison between different ML and AI algorithms for pattern recognition and classification algorithms is also discussed. The discussion then establishes application cases of smart ML algorithms, which provide efficient data processing methods, for the design of smart gas sensors that are highly selective. In addition, the challenges and limitations of ML in gas sensor applications are critically discussed. The study shows the importance of ML with the need for structural optimization to develop and improve smart, sensitive, and selective sensors.

关键词： Sensors Gas detectors machine learning Intelligent sensors Electrodes Sensor phenomena and characterization machine learning algorithms algorithms IDEs structures machine learning (ML) materials selectivity sensitivity smart gas sensor

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