“…METHODS: GCA patients who underwent GCs therapy and regular follow-up visits for at least 12 months, were retrospectively analyzed and used for implementing 3 ML algorithms, namely, Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). The outcome of interest was disease relapse within 3 months during GCs tapering. …”
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“…The overall accuracy was 0.703, 0.768, 0.794, and 0.761 for logistic regression, random forest, eXtreme Gradient Boosting, and support vector machine, respectively. …”
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“…Different machine learning approaches (Random Forest, XGBoost, and ElasticNet) were applied to model the unique T2DM/Pre-T2DM metabolic patterns and contrasted with their effectiness to differentiate T2DM/Pre-T2DM from controls. …”
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“…Fecal microbiota were categorized using specific primers targeting the V1–V3 region of 16S rDNA, and serum metabolites were characterized by NMR-spectroscopic analysis and by multivariate statistical analysis, Spearman rank correlations, and Random Forest models. RESULTS: We found higher α-diversity, represented by the Shannon index value (HITB-pre 5.9 [± 0.4]; HITB-post 6.4 [± 0.4], p = 0.007), (HIT-pre 5.5 [± 0.6]; HIT-post 5.9 [± 0.6], p = 0.015), after the end of the training program in both groups independently of Bryndza cheese consumption. …”
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“…An eight-lncRNA signature was identified by random survival forest algorithm and multivariate Cox regression analysis. …”
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“…Four ML methods, including support vector regression (SVR), kernel ridge regression (KRR), random forest (RF) and Adaboost.R2 were implemented. Through 20 replicates of fivefold cross-validation (CV) and one prediction for younger individuals, the utility of ML methods in genomic prediction was explored. …”
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“…Logistic regression (LR), random forest (RF), gradient boosting machine (GBM), and feedforward neural network (FNN) algorithms were applied. …”
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“…Commensal Butyricicoccus, Faecalibacterium, Ruminococcus, Collinsella, and Coriobacterium were key microbial markers that distinguished healthy and diarrheic gut microbiomes. Random forest machine-learning algorithm and Spearman correlation results indicated that enriched UDCA, short-chain fatty acids (SCFAs), and other prebiotics were strongly positively correlated with these five bacterial genera. …”
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“…By logistic regression and random forest analyses, AUCs were 0.80 (0.73, 0.87) and 0.78 (0.70, 0.85), respectively. …”
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“…BACKGROUND: Needle rust caused by the fungus Chrysomyxa rhododendri causes significant growth decline and increased mortality of young Norway spruce trees in subalpine forests. Extremely rare trees with enhanced resistance represent promising candidates for practice-oriented reproduction approaches. …”
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“…In addition to a number of newly introduced optimizations its most significant change is the replacement of CARE 1.0’s hand-crafted correction conditions with a novel classifier based on random decision forests trained on Illumina data. This results in up to two orders-of-magnitude fewer false-positive corrections compared to other state-of-the-art error correction software. …”
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“…Associations between treatment effect estimates and trial-level characteristics were presented using forest plots. RESULTS: Eighty ME studies were included, with 25/80 (31%) being published after 2015. …”
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“…BACKGROUND: Lignocellulosic biomasses produced from agriculture and forest-based industries are the cheapest or negative-cost biomass with a great potential for biotransformation to value-added bioproducts. …”
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“…Disparities in social determinants, including race, sex, marital status, insurance types and languages, among patients identified by six available sepsis criteria were revealed by forest plots with 95% confidence intervals. Sepsis patients were then identified by the Sepsis-3 criteria. …”
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“…BACKGROUND: Onchocerciasis control using ivermectin alone has been achieved in some endemic savannah zones of Africa. In the forest regions, the co-endemicity with Loa loa has led to severe adverse events (SAEs) resulting in poor adherence of community members to ivermectin mass drug administration (MDA). …”
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“…MATERIALS AND METHODS: Blood samples (3 mL) were collected from a population of M. fascicularis (92 heads) inhabiting a small forest with irrigated rice field nearby (wetland area) in Ubud, Gianyar, and from two populations in dryland areas with no wet rice field (Uluwatu, Badung, and Nusa Penida, Bali Province, Indonesia). …”
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“…A 15 CpG SVZ methylation signature (SVZM) was discovered based on clustering and random forest analysis. One third of CpG in the SVZM were associated with MAB21L2/LRBA. …”
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“…Machine learning approaches (Random Forrest, LASSO) selected important features from nearly 1,000 available candidate predictors and used them to generate 4 distinct models, including non-regularized and LASSO regression, random forest, and gradient boosting. Candidate predictors from the best performing models, for each outcome, were used as independent variables in multiple linear regression models. …”
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“…RESULTS: Between October 2018 and December 2020, through the IP 16,902 forest packs and 293,090 long-lasting insecticide treated nets were distributed. …”
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“…The pooled prevalence of dual contraception use and the odds ratio (OR) with a 95% confidence interval was presented by using forest plots. RESULT: Eleven studies were included in this review, with a total of 4083 women living with HIV in Ethiopia. …”
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