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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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Evaluation of supervised machine learning algorithms in predicting the poor anticoagulation control and stable weekly doses of warfarin

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INTERNATIONAL JOURNAL OF CLINICAL PHARMACY 2023年第1期45卷 79-87页

作者： Sridharan, Kannan Ramanathan, Murali Al Banna, Rashed Arabian Gulf Univ Coll Med & Med Sci Dept Pharmacol & Therapeut Manama Bahrain Univ Buffalo State Univ New York Sch Pharm & Pharmaceut Sci Dept Pharmaceut Sci Buffalo NY USA Salmaniya Med Complex Dept Cardiol Manama Bahrain

Background machine learning algorithms (MLAs) carry a huge potential in identifying predicting factors and are being explored for their utility in the field of personalized medicine. Aim We aimed to investigate MLAs for identifying predictors (clinical and genetic) of poor anticoagulation status (ACS) and stable weekly warfarin dose (SWWD). Method Clinical factors, in addition to the CYP2C9, VKORC1, and CYP4F2 genotypes, were obtained for patients receiving warfarin for at least the previous six months. The C5.0 decision tree classification algorithm was used to predict poor ACS while classification and regression tree analysis (CART), in addition to the Chi-square automatic interaction detector (CHAID), was used to predict SWWD. The percentage of patients within 20% of the actual dose, root mean squared error (RMSE), and area under the receiver-operating characteristics curve (AUROC) were identified as performance indicators of the models. Results In the C5.0 classification decision tree, the CYP4F2 genotype was the strongest predictor of ACS (AUROC = 0.53). In the CART analysis of SWWD, VKORC1 polymorphisms were the most significant predictor, followed by the CYP2C9 genotype (percentage of patients within 20% of the actual dose = 38.2%, RMSE = 13.6). For the CHAID algorithm, the percentage of patients within 20% of the actual dose was 49%, while the RMSE was found to be 13.4. Conclusion Genetic and non-genetic predictive factors were identified by the MLAs for ACS and SWWD. Further, the need to externally validate the MLAs in a prospective study was highlighted.

关键词： Anticoagulants Artificial intelligence Decision trees machine learning algorithms Pharmacogenetics

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Determinants of carbon emissions in Africa: new evidence based on machine learning algorithms

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2024年 1-34页

作者： Ayimadu, Edwin Twum Liu, Yaolin Asante, Isaac Osei Dunee, Donatus Mwakipunda, Grant Charles Wuhan Univ Sch Resource & Environm Sci 299 Luoyu Rd10 Wuchang Dist Wuhan Hubei Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan 430072 Peoples R China Minist Educ Key Lab Aerosp Informat Secur & Trusted Comp Wuhan 430072 Peoples R China Wuhan Univ Res Inst Environm Law 299Bayi Rd Wuhan Hubei Peoples R China China Univ Geosci Key Lab Theory & Technol Petr Explorat & Dev Hubei Wuhan 430074 Peoples R China

Balancing economic expansion and environmental sustainability in Africa is critical, as rising CO2 emissions threaten the continent's commitment to global climate goals. Despite widespread acknowledgment, a significant gap in understanding the sector-specific contributions to emissions persists. This empirical study examines the impact of economic expansion, total natural resource rents, and energy consumption on CO2 emissions in 48 African countries from 2000 to 2022. The study utilized a combination of linear and nonlinear machine learning models, OLS-linear regression, random forest, extra trees, histogram gradient boosting, and decision trees to assess the intricate relationships between these variables. Ensemble methods, incorporating boosting and bagging techniques, were also employed to enhance model accuracy. Additionally, impulse response and variance decomposition analyses were conducted to forecast the long-term impact of variable changes on CO2 over the next decade. The findings reveal several critical insights: First, economic expansion and energy consumption promote CO2 emissions in the study area. The same hypothesis was repeated for their interactive effect on CO2. Second, total natural resource rents positively impact environmental sustainability across the continent. The analysis indicates that if these trends are left unchecked, they could severely undermine efforts to achieve environmental sustainability in Africa. Third, the impulse response and variance decomposition results indicate that over the next ten years, variations in energy use will have a more substantial impact on CO2 than fluctuations in natural resource rents and economic expansion. We, therefore, suggested a valuable baseline for economy-energy-resources-carbon nexus policy implications to reduce CO2 across the continent.

关键词： Environmental sustainability Emissions machine learning algorithms Energy consumption Variance decomposition

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Tail Risk Early Warning System for Capital Markets Based on machine learning algorithms

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COMPUTATIONAL ECONOMICS 2022年第3期60卷 901-923页

作者： Zhang, Zongxin Chen, Ying Fudan Univ Sch Econ Shanghai Peoples R China

Scientific and effective tail risk measurement and early warning are key points and difficulties in the identification and control of major risks in capital markets. In this paper, we use the autoregressive conditional Frechet model (AcF) to construct a tail risk measurement index for the capital market in China. The tail risk status identified by the scientific index method is used as a monitoring anchor to construct and optimize a tail risk early warning model based on machine learning algorithms. The study yields three findings. (1) The AcF model can overcome the shortcomings of traditional models in tail risk measurement and significantly improve the tail risk measurement efficiency of the capital market. (2) Tail risk synergies between equity and bond markets are significantly stronger than yield synergies, and the tail risk measure index has the role of a leading indicator of significant risk in capital markets. (3) Based on the joint test of risk status and crisis identification efficiency, the Logit model of crisis identification fails whereas the tail risk warning model optimized by machine learning algorithms can accurately identify crises and significant risks. The optimal early warning model pairings for the stock market and bond market are the oversampling-random forest algorithm and the double sampling-random forest algorithm, respectively, with out-of-sample crisis warning accuracies of 81.94% and 90.20%, respectively.

关键词： Tail Risk Measurement Risk Warning AcF Model machine learning algorithms

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Value of machine learning algorithms for predicting diabetes risk: A subset analysis from a real-world retrospective cohort study

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JOURNAL OF DIABETES INVESTIGATION 2023年第2期14卷 309-320页

作者： Mao, Yaqian Zhu, Zheng Pan, Shuyao Lin, Wei Liang, Jixing Huang, Huibin Li, Liantao Wen, Junping Chen, Gang Fujian Med Univ Fujian Prov Hosp Dept Internal Med South BranchShengli Clin Med Coll Fuzhou Peoples R China Fujian Med Univ Fujian Prov Hosp Dept Endocrinol Shengli Clin Med Coll Fuzhou Peoples R China Fujian Acad Med Fujian Prov Key Lab Med Anal Fuzhou Peoples R China

Aims/Introduction To compare the application value of different machine learning (ML) algorithms for diabetes risk prediction. Materials and Methods This is a 3-year retrospective cohort study with a total of 3,687 participants being included in the data analysis. Modeling variable screening and predictive model building were carried out using logistic regression (LR) analysis and 10-fold cross-validation, respectively. In total, six different ML algorithms, including random forests, light gradient boosting machine, extreme gradient boosting, adaptive boosting (AdaBoost), multi-layer perceptrons and gaussian naive bayes were used for model construction. Model performance was mainly evaluated by the area under the receiver operating characteristic curve. The best performing ML model was selected for comparison with the traditional LR model and visualized using Shapley additive explanations. Results A total of eight risk factors most associated with the development of diabetes were identified by univariate and multivariate LR analysis, and they were visualized in the form of a nomogram. Among the six different ML models, the random forests model had the best predictive performance. After 10-fold cross-validation, its optimal model has an area under the receiver operating characteristic value of 0.855 (95% confidence interval [CI] 0.823-0.886) in the training set and 0.835 (95% CI 0.779-0.892) in the test set. In the traditional LR model, its area under the receiver operating characteristic value is 0.840 (95% CI 0.814-0.866) in the training set and 0.834 (95% CI 0.785-0.884) in the test set. Conclusions In the real-world epidemiological research, the combination of traditional variable screening and ML algorithm to construct a diabetes risk prediction model has satisfactory clinical application value.

关键词： Diabetes machine learning algorithms Predictive model

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Delineation of groundwater potential zones of upper Godavari sub-basin of India using bi-variate, MCDM and advanced machine learning algorithms

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GEOCARTO INTERNATIONAL 2022年第27期37卷 15063-15093页

作者： Choudhary, Sourav Pingale, Santosh Murlidhar Khare, Deepak Indian Inst Technol Roorkee Dept Water Resources Dev & Management Roorkee Uttar Pradesh India Natl Inst Hydrol Roorkee Uttar Pradesh India

Sustainable management of groundwater resource is a most critical due to its over exploitation and ascending stress by industrial and socio-economic factors. It is utmost important to manage this precious resource by properly identifying the suitable Groundwater Potential Zones (GPZ). Therefore, the main aim of the present study is to delineate the GPZ in the upper Godavari sub-basin of India by employing different bi-variate, Multi Criteria Decision Making (MCDM), ensembled, and machine learning (ML) models. These models include Weight of Evidence (WoE) (bi-variate), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) (MCDM), Fuzzified Functional Ratio (F-FR) (bi-variate), Extreme Gradient Boosting (XGB) (ML) and Extremely randomized Trees (ET) ML. The ensembled model featured different combination of WoE, TOPSIS and F-FR for mapping the enhanced accuracy in predicting the GPZ. A total of 15 groundwater factors were considered where 75% of the data were selected as training data and the rest 25% as validation data. These data were used to produce the ensembled ML models. The result of the model was plotted in terms of area under curve (AUC)-Receiver Operating Characteristics (ROC) curve and selected best model. The AUC-ROC result of the obtained model was found to be WoE oE models. The result of the model was plotted in termWoE_TOPSIS = 94%, WoE_F-FR = 93%, TOPSIS_F-FR = 94% and WoE_TOPSIS_F-FR = 95%. Results clearly indicate the improved accuracy of ensembled bi-variate and MCDM model over advanced ML model. The predicted statistical properties of the ensembled model also resembled ML models and a high correlation was observed. Thus, the ensembled model can be used over advanced ML models for delineating the GPZ mapping.

关键词： Groundwater potential bi-variate and MCDM models machine learning algorithms Godavari basin India

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machine learning algorithms for lamb survival

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 182卷 105995-105995页

作者： Odevci, B. B. Emsen, E. Aydin, M. N. Kadir Has Univ Management Informat Syst TR-34083 Istanbul Turkey Ataturk Univ Dept Anim Sci TR-25240 Erzurum Turkey

Lamb survival is influenced by the culmination of a sequence of often interrelated events including genetics, physiology, behaviour and nutrition, with the environment providing an overarching complication. machine learning algorithms offer great flexibility with regard to problems of complex interactions among variables. The objective of this study was to use machine learning algorithms to identify factors affecting the lamb survival in high altitudes and cold climates. Lambing records were obtained from three native breed of sheep (Awassi = 50, Morkaraman = 50, Tuj = 50) managed in semi intensive systems. The data set included 193 spring born lambs out of which 106 lambs were sired by indigenous rams (n = 10), and 87 lambs were sired by Romanov Rams (n = 10). Factors included were dam body weight at lambing, age of dam, litter size at birth, maternal and lamb be-haviors, and lamb sex. Individual and cohort data were combined into an original dataset containing 1351 event records from 193 individual lambs and 750 event records from 150 individual ewes. Classification algorithms applied for lamb survival were Bayesian Methods, Artificial Neural Networks, Support Vector machine and Decision Trees. Variables were categorized for lamb survival, lamb behavior, and mothering ability. Random-Forest performed very well in their classification of the mothering ability while SMO was found best in predicting lamb behavior. REPtree tree visualization showed that grooming behavior is the first determinant for mothering ability. Classification Trees performed best in lamb survival. Our results showed that Classification Trees clearly outperform others in all traits included in this study.

关键词： machine learning algorithms Lamb survival

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machine learning algorithms for Modeling and Mapping of Groundwater Pollution Risk: A Study to Reach Water Security and Sustainable Development (Sdg) Goals in a Mediterranean Aquifer System

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REMOTE SENSING 2022年第10期14卷 2379-2379页

作者： Ijlil, Safae Essahlaoui, Ali Mohajane, Meriame Essahlaoui, Narjisse Mili, El Mostafa Van Rompaey, Anton Moulay Ismail Univ Fac Sci Dept Geol Lab Geoengn & EnvironmRes Grp Water Sci & Enviro BP 11201 Meknes Morocco Moulay Ismail Univ Fac Sci Dept Biol Res Grp Soil & Environm Microbiol BP 11201 Meknes Morocco Katholieke Univ Leuven Dept Earth & Environm Sci Geog & Tourism Res Grp Celestijnenlaan 200E B-3001 Heverlee Belgium

Groundwater pollution poses a severe threat and issue to the environment and humanity overall. That is why mitigative strategies are urgently needed. Today, studies mapping groundwater risk pollution assessment are being developed. In this study, five new hybrid/ensemble machine learning (ML) models are developed, named DRASTIC-Random Forest (RF), DRASTIC-Support Vector machine (SVM), DRASTIC-Multilayer Perceptron (MLP), DRASTIC-RF-SVM, and DRASTIC-RF-MLP, for groundwater pollution assessment in the Saiss basin, in Morocco. The performances of these models are evaluated using the Receiver Operating Characteristic curve (ROC curve), precision, and accuracy. Based on the results of the ROC curve method, it is indicated that the use of hybrid/ensemble machine learning (ML) models improves the performance of the individual machine learning (ML) algorithms. In effect, the AUC value of the original DRASTIC is 0.51. Furthermore, both hybrid/ensemble models, DRASTIC-RF-MLP (AUC = 0.953) and DRASTIC-RF-SVM, (AUC = 0.901) achieve the best accuracy among the other models, followed by DRASTIC-RF (AUC = 0.852), DRASTIC-SVM (AUC = 0.802), and DRASTIC-MLP (AUC = 0.763). The results delineate areas vulnerable to pollution, which require urgent actions and strategies to improve the environmental and social qualities for the local population.

关键词： groundwater pollution risk vulnerability DRASTIC machine learning algorithms bivariate statistic Saiss basin

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Guest Editorial Advanced machine learning algorithms for Biomedical Data and Imaging - Part II

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第1期27卷 188-189页

作者： Tanveer, M. Lin, Chin-Teng Singh, Amit Kumar Indian Inst Technol Indore Indore India Univ Technol Sydney Sydney NSW Australia Natl Inst Technol Patna Patna Bihar India

The papers in this special section (Part II) focus on advanced machine learning algorithms for biomedical data and imaging. The papers in Part II aim at bringing together contributions from both academia and industry ... 详细信息

关键词： Special issues and sections machine learning algorithms Biomedical imaging Bioinformatics Functional magnetic resonance imaging Ultrasonic imgaging Telemedicine Feature extraction Brain modeling

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The electrocardiogram endeavour: from the Holter single-lead recordings to multilead wearable devices supported by computational machine learning algorithms

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EUROPACE 2020年第1期22卷 19-23页

作者： Vardas, Panos Cowie, Martin Dagres, Nikolaos Asvestas, Dimitrios Tzeis, Stylianos Vardas, Emmanuel P. Hindricks, Gerhard Camm, John Hygeia Hosp Grp Heart Sect Athens Greece Imperial Coll London London England Univ Leipzig Heart Ctr Leipzig Dept Electrophys Leipzig Germany Mitera Gen Hosp Hygeia Grp Cardiol Dept Athens Greece 251 Airforce Gen Hosp Athens Greece St Georges Univ London London England

This review aims to provide a comprehensive recapitulation of the evolution in the field of cardiac rhythm monitoring, shedding light in recent progress made in multilead ECG systems and wearable devices, with emphasis on the promising role of the artificial intelligence and computational techniques in the detection of cardiac abnormalities.

关键词： Electrocardiogram Electrocardiography machine learning algorithms Multilead wearable devices

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