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  1. 36821
    “…A descriptive summary of the data and possible meta-analysis was performed. Forest plots were generated to pool estimates based on each of the study outcomes. …”
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    The impact of facemask on patients with COPD: A systematic review and meta-analysis
  2. 36822
    “…Using weighted correlation network analysis (WGCNA) and multiple machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), CoxBoost, and random forest, we constructed a claudin-TME related (CTR) risk signature. …”
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    Identification of a claudin-low subtype in clear cell renal cell carcinoma with implications for the evaluation of clinical outcomes and treatment efficacy
  3. 36823
    “…Integrating electronic medical records, metabolomics, and microbiome data, four machine learning methods was used, including random forest algorithm, artificial neural network, support vector machine with the linear kernel and radial basis function kernel. …”
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    Urine metabolomics and microbiome analyses reveal the mechanism of anti-tuberculosis drug-induced liver injury, as assessed for causality using the updated RUCAM: A prospective study
  4. 36824
    “…Several machine learning algorithms including multivariable logistic regression classifier (LRC), support vector classifier (SVC), random forest classifier (RFC), gradient boosting classifier (GBC), and multilayer perceptron classifier (MLPC) were optimized for HT prediction in a randomly selected half of the sample (training set) and tested in the other half of the sample (testing set). …”
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    Machine learning approach for hemorrhagic transformation prediction: Capturing predictors' interaction
  5. 36825
    “…Biomarkers were then screened using a random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM). …”
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    Comprehensive bioinformatics analysis reveals biomarkers of DNA methylation-related genes in varicose veins
  6. 36826
    “…Three variable selection methods including randomForestSRC were performed. As AI/ML algorithms for 4-group classification, GUIDE and proportional odds model were used. …”
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    Machine learning models to predict the maximum severity of COVID-19 based on initial hospitalization record
  7. 36827
    “…In this study, 12 significant m6A regulators were filtered out between healthy controls and IPF patients using GSE38958 dataset. Random forest algorithm was used to identify 11 candidate m6A regulators to predict the incidence of IPF. …”
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    Effect of M6A regulators on diagnosis, subtype classification, prognosis and novel therapeutic target development of idiopathic pulmonary fibrosis
  8. 36828
    “…STATA15.0 was used to conduct heterogeneity analysis, sensitivity analysis, forest plot analysis, and publication bias analysis. …”
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    Online Artículo Texto
    RT-based combination therapy for brain metastasis from NSCLC with non-EGFR mutation/ALK gene rearrangement: A network meta-analysis
  9. 36829
    “…Differentially expressed immune cell-related genes were obtained by CIBERSORT, WGCNA, and the identification of differentially expressed genes. Random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM) were then constructed and the biomarkers for EM were determined. …”
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    Bioinformatics identification and validation of biomarkers and infiltrating immune cells in endometriosis
  10. 36830
    por Wu, Huiyuan, Li, Fenfen, Zhang, Fenghao
    Publicado 2022
    “…Review Manager 5.4 was used to evaluate the effects of the results among selected articles. Forest plots and funnel plots were also performed. …”
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    The efficacy of mindfulness-based stress reduction vs. standard or usual care in patients with breast cancer: a systematic review and meta-analysis of randomized controlled trials
  11. 36831
    “…The screened variables were incorporated into a random survival forest (RSF) model for the prediction of 1-, 3-, and 5-year survival in patients with NSCLC. …”
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    Prognostic factors of survival in patients with non-small cell lung cancer: a competing risk model using the SEER database
  12. 36832
    “…PATIENTS AND METHODS: The current study retrospectively included data from 1,255 patients with COPD. Random survival forest plots and Cox proportional hazards regression were used to screen for independent risk factors in patients with COPD. …”
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    Prediction of individual mortality risk among patients with chronic obstructive pulmonary disease: a convenient, online, individualized, predictive mortality risk tool based on a retrospective cohort study
  13. 36833
    “…Next, we combined bioinformatics techniques with machine learning methodologies such as random forest algorithms and support vector machines to screen for and identify diagnostic markers of AVC. …”
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    Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks
  14. 36834
    “…We sought to emulate a randomized controlled trial using the targeted maximum likelihood (TMLE) approach with logistic regression, random forests, multilayer perceptrons, and XGBoost to mitigate confounding by indication and to model the outcome (Figure 1). …”
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    2238. Estimating the impact of antibiotic exposure on antibiotic resistance in uncomplicated UTI using machine learning causal inference
  15. 36835
    “…Differentially expressed PCD-related genes between successful and failed treatment samples were analyzed, and Least absolute shrinkage and selection operator (LASSO) logistic regression and random forest were used to screen PCD-related molecular markers for ECMO treatment in AMI combined with cardiogenic shock. …”
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    Identification of key programmed cell death-related genes and immune infiltration in extracorporeal membrane oxygenation treatment for acute myocardial infarction based on bioinformatics analysis
  16. 36836
    “…The performance of EBM in predicting pCR on the test dataset was evaluated using ROC AUC and compared with that of three state-of-the-art black-box models: extreme gradient boosting (XGB), random forest (RF) and support vector machine (SVM). The predictions of all black-box models were interpreted using Shapley additive explanations. …”
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    Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma
  17. 36837
    “…We show that the Florida shovel-tusked gomphotheres browsed on leaves and twigs, and thus, most likely occupied a dietary niche similar to living forest elephants. We demonstrate that they used their tusks for a wide variety of purposes. …”
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    Dietary Habits and Tusk Usage of Shovel-Tusked Gomphotheres from Florida: Evidence from Stereoscopic Wear of Molars and Upper and Lower Tusks
  18. 36838
    “…Variable simplification models were constructed after screening important variables using the random forest recursive feature elimination method, and the performance of different machine learning methods and two types of modeling strategies were evaluated using multiple indicators. …”
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    Identifying Patients at Risk of Acute Kidney Injury among Patients Receiving Immune Checkpoint Inhibitors: A Machine Learning Approach
  19. 36839
    “…SIMPLE SUMMARY: The nutritional content of food plants can, to a large extent, determine the physical attributes of herbivorous insects, from growth rates to the need for defenses against predators. In forests, tree species richness may influence these plant-mediated effects through increasing variation in the nutritional quality that herbivorous insects encounter. …”
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    Leaf Nutritional Content, Tree Richness, and Season Shape the Caterpillar Functional Trait Composition Hosted by Trees
  20. 36840
    “…The above prognostic DEGs were analyzed by Random Survival Forest and least absolute shrinkage and selection operator (Lasso) analysis to create the TRP signature. …”
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    A transient receptor potential channel-related model based on machine learning for evaluating tumor microenvironment and immunotherapeutic strategies in acute myeloid leukemia
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