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  1. 36221
    “…Odds ratio (OR) with a 95% confidence interval (CI) was presented using forest plots. A total of twelve studies were included in this study. …”
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    Prevalence and determinants of uterine rupture in Ethiopia: a systematic review and meta-analysis
  2. 36222
    por Moody, Laura, Chen, Hong, Pan, Yuan-Xiang
    Publicado 2020
    “…Methods that retain more information, such as the magnitude of expression change and within-class variance, yielded higher classification accuracy using a random forest model. We then demonstrate two powerful applications of gene pairs by first performing large-scale integration of 52 breast cancer datasets consisting of 10,350 patients. …”
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    Considerations for feature selection using gene pairs and applications in large-scale dataset integration, novel oncogene discovery, and interpretable cancer screening
  3. 36223
    “…We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for > 10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). …”
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    Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets
  4. 36224
    “…Predictive accuracy of five common machine-learning algorithms (logistic regression, random forest, gradient boosting machines, neural networks and ensemble learning) were assessed for detecting FH. …”
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    Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care
  5. 36225
    por Nath, Abhigyan, Leier, André
    Publicado 2020
    “…When K-means based sampling of negative datasets is applied to mitigate the inadequacies of random sampling, random forest (RF) together with the combined feature set of atomic composition, physicochemical-2grams and two different representations of evolutionary information performs best. …”
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    Improved cytokine–receptor interaction prediction by exploiting the negative sample space
  6. 36226
    “…RESULTS: There was high variation among sites in mosquito community structure, and variation in site level richness that correlated with scrub forest, agricultural, and urban land covers. The four most abundant species were Aedes taeniorhynchus, Culex quinquefasciatus, Aedes aegpyti and Deinocerites magnus, and their relative abundance varied with season and land cover. …”
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    Effects of seasonality and land use on the diversity, relative abundance, and distribution of mosquitoes on St. Kitts, West Indies
  7. 36227
    “…We find that high biodiversity in tropical peat-swamp forests, including presence of many potential vertebrate and invertebrate vectors, combined, in places, with high levels of habitat disruption and wildlife harvesting represent suitable conditions for potential zoonotic EID (re-)emergence. …”
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    Tropical peatlands and their conservation are important in the context of COVID-19 and potential future (zoonotic) disease pandemics
  8. 36228
    “…Three machine learning methods, classification and regression tree (CART), random forest (RF), and gradient boosting decision tree (GBDT), were used to develop models to predict pLOS, which is longer than the average LOS, in PD patients. …”
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    Development of a scoring tool for predicting prolonged length of hospital stay in peritoneal dialysis patients through data mining
  9. 36229
    “…Four ML-assisted models were developed from 16 clinical features using logistic regression (LR), logistic regression optimized by least absolute shrinkage and selection operator (Lasso) regularization (Lasso-LR), random forest (RF), and support vector machine (SVM). The area under the curve (AUC) was applied to determine the model with the highest discrimination. …”
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    Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-Support Models
  10. 36230
    “…Heterogeneity in studies will be explored by visual inspection of forest plots as well as using the Higgins and Thompson I(2) method. …”
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    Comparison of respiratory pathogen colonization and antimicrobial susceptibility in people with cystic fibrosis bronchiectasis versus non-cystic fibrosis bronchiectasis: a protocol for a systematic review
  11. 36231
    “…BACKGROUND: The red sea urchin Mesocentrotus franciscanus is an ecologically important kelp forest herbivore and an economically valuable wild fishery species. …”
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    Gene expression patterns of red sea urchins (Mesocentrotus franciscanus) exposed to different combinations of temperature and pCO(2) during early development
  12. 36232
    “…Meta-analysis was performed using RevMan 5.3 and Stata 16.0 software, and forest plots, funnel plots, and sensitivity analysis were also conducted. …”
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    Effect of obesity on the prognosis and recurrence of prostate cancer after radical prostatectomy: a meta-analysis
  13. 36233
    “…BACKGROUND: Forest trees have important economic and ecological value. …”
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    An improved and efficient method of Agrobacterium syringe infiltration for transient transformation and its application in the elucidation of gene function in poplar
  14. 36234
    “…RESULTS: The results show that the accuracy rate of the prediction model using Convolutional Neuron Networks (CNN), Random Forest (RF), Linear-Regression, Support Vector Regression (SVR), Back Propagation Neural Network(BPNN), and the proposed hybrid-LSTM at the 40th pregnancy week for first delivery were 0.498, 0.662, 0.670, 0.680, 0.705 and 0.793, respectively. …”
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    Fetal birthweight prediction with measured data by a temporal machine learning method
  15. 36235
    “…In this study, we investigate the capacity of machine learning, particularly support vector machines (SVM) and random forests (RF), for estimating MBF from tissue impulse response signal in an animal model. …”
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    Quantification of Myocardial Blood Flow by Machine Learning Analysis of Modified Dual Bolus MRI Examination
  16. 36236
    “…The whole tumor enhancementwas manually drawn on the T(1)CE image, and a total of texture 9675 features were extracted and fed to a two-step feature selection scheme. A random forest (RF) classifier was trained to separate the patients by their outcomes.The diagnostic efficacies of the radiomics modeland radiologist assessment were further compared by using theaccuracy (ACC), sensitivity and specificity. …”
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    Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T(1)-weighted Contrast-enhanced Imaging
  17. 36237
    “…Lastly, we found 26 more introduced species sold at VOP compared to 1984. CONCLUSIONS: Forest degradation and deforestation, prevalence of diseases, and methodological factors may play a role in the differences we found in our survey compared to 1984. …”
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    What’s in a name? Revisiting medicinal and religious plants at an Amazonian market
  18. 36238
    “…Finally, we constructed a random forest (RF) model that was trained and tested against existing datasets from developed countries. …”
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    The colorectal cancer-associated faecal microbiome of developing countries resembles that of developed countries
  19. 36239
    “…The diverse flora of the Atlantic Forest is fertile ground for discovering new chemical structures with insecticidal activity. …”
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    Insecticidal and oviposition deterrent effects of essential oils of Baccharis spp. and histological assessment against Drosophila suzukii (Diptera: Drosophilidae)
  20. 36240
    “…Other factors associated with likelihood to become or remain an active focus include previous classification as an active focus, presence of Plasmodium falciparum infections, level of forest disturbance, and location in border provinces. …”
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    A foci cohort analysis to monitor successful and persistent foci under Thailand’s Malaria Elimination Strategy
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