“…A multimodal model with the Xception model as image feature extraction and machine learning algorithms [random forest (RF), support vector machine (SVM), dense neural network (DNN), and others] was applied. …”
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“…Five machine learning algorithms, Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Decision tree (DT), were used in combination with preoperative clinical characteristics and laboratory data to establish a predictive model of LCI in patients with a femoral neck fracture. …”
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“…The cut-off value of treatment failure was calculated using the minimum P-value approach. Random survival forest (RSF) model was to simulate the cumulative probabilities of treatment failure (locoregional recurrence and /or distant metastasis) over-time, as well as the monthly time-specific, event-occurring probabilities, for patients at different treatment groups. …”
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“…Seven machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K Nearest Neighbor (KNN), lightGBM, XGBoost, and Multilayer Perception (MLP) were used. …”
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“…Resistant starch also differentially modified fecal metabolite concentrations with relevance to GI and overall host health (increased butyrate; decreased propionate and protein catabolites - branched-chain fatty acids; phenols and indoles; ammonia) and reduced blood cholesterol, which correlated strongly with microbial taxa and KO terms, and allowed for a high predictive efficiency of diet groups by random forest analysis. CONCLUSION: Even though domestic cats and other carnivores evolved by eating low-carbohydrate diets rich in protein and fat, our results demonstrate that the feline microbiome and metabolite profiles are highly responsive to dietary change and in directions that are predictable. …”
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“…We performed a narrative synthesis of the data, using forest plots to summarize study findings, and stratified data presentation to explore the potential association of risk of bias, case definition, and reference period with estimates of prevalence and incidence of shoulder pain. …”
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“…BACKGROUND: The foot transit of migrant peoples originating from the Caribbean, South America, Asia, and Sub-Saharan Africa through the Darién Forest (DF) in Eastern Panamá towards North America has increased in recent years from approximately 30,000 people/year to >133,000 in 2021. …”
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“…If a meta-analysis cannot be performed, we will conduct a narrative synthesis of the results and produce forest plots for individual studies. DISCUSSION: This protocol outlines the methods of a systematic review that will determine the effect of biomechanical foot-based interventions on patellofemoral joint loads. …”
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“…A potentially novel actinobacterium isolated from forest soil, Streptomyces sp. KSF103 was evaluated for its insecticidal effect against several mosquito species namely Aedes aegypti, Aedes albopictus, Anopheles cracens and Culex quinquefasciatus. …”
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“…Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. …”
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“…Differential features were used to construct random forests classifiers. RESULTS: IBS was associated with global alterations in microbiome composition by 16S rRNA sequencing and metatranscriptomics, and in microbiome function by predicted metagenomics, metatranscriptomics, and metabolomics. …”
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“…Summary estimates are presented using tables, funnel plots, forest plots, histograms, violin plots, and line plots. …”
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“…However, model interactions showed they selected sites with lower levels of greenness when in forest (both seasons) and shrubland (fall only), which may reflect their preference for more open habitats or represent a trade-off in selection between habitat type and productivity. …”
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“…We used Stata (version 15.0) for forest graph. RESULTS: Thirteen studies were included in this meta-analysis, involving a dose range of 66–70 Gy for the standard treatment regimen and <66 Gy for the reduced-dose group. …”
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“…A ResUNet model was modified to automatically delineate lung contours and infectious lung opacities on CT scans, after which a random forest predicted the CO-RADS score. After internal testing, the model was externally validated on a multicenter dataset (n = 400) by independent researchers. …”
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“…Four machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Deep Learning (DL), were compared to test the performance of vegetation coverage detection. 5 spectral values (Red, Green, Blue, NIR, Red edge) and 16 VIs were selected to perform variable importance analysis on the best detection models. …”
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“…METHODS: We performed dimensionality reduction in gene expression data using a semi-automated preprocessing systematic gene selection procedure using Statistically Equivalent Signature (SES), a causality-based feature selection algorithm, followed by Boosted Regression Trees (XGBoost) and Random Forest to train the machine learning classifiers. …”
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“…In addition to this, similar results were achieved using another machine learning technique namely random forest. Simultaneously predictive models performed equally well during five-fold cross validation. …”
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“…Radiomics signatures were constructed with random forest survival models in the training cohort and compared against baseline clinical characteristics through Cox regression and nonparametric testing of concordance indices (C-indices). …”
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“…Furthermore, a five-gene m(7)G regulator diagnostic signature, NUDT16, NUDT4, CYFIP1, LARP1, and DCP2, which can easily distinguish HF patients and NFDs, was established by cross-combination of three machine learning methods, including best subset regression, regularization techniques, and random forest algorithm. The diagnostic value of five-gene m(7)G regulator signature was further validated in human samples through quantitative reverse-transcription polymerase chain reaction (qRT-PCR). …”
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