“…The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, support vector machine (SVM), eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and decision tree (DT) ranged from 0.765 to 0.877 in the training set and from 0.716 to 0.862 in the testing set, respectively. …”
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“…These models include the BERT base model, PubMedBERT (abstracts + full text), PubMedBERT (only abstracts), Unified Medical Language System (UMLS) BERT, Bio BERT, Bio-clinical BERT, logistic regression, support vector machine, and random forest. The rule-based model used for weak supervision was tested on the GSC for comparison. …”
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“…We observed that random forest classification models trained on deep represented features outperformed models trained on unrepresented data for all pain rating scales. …”
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“…In our imputation analysis we demonstrate that Single Imputation methods that borrow information from correlated proteins such as Generalized Ridge Regression (GRR), Random Forest (RF), local least squares (LLS), and a Bayesian Principal Component Analysis methods (BPCA), are able to estimate missing protein abundance values with great accuracy. …”
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“…Of the nine ML models tested, the best performance was achieved with the random forest (RF) model, with an A.C. of 0.81, an accuracy of 85% and a precision of 62% in the validation cohort. …”
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“…BACKGROUND: The root mycobiome plays a fundamental role in plant nutrition and protection against biotic and abiotic stresses. In temperate forests or meadows dominated by angiosperms, the numerous fungi involved in root symbioses are often shared between neighboring plants, thus forming complex plant-fungus interaction networks of weak specialization. …”
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“…METHODS: By screening differentially expressed genes (DEGs), constructing random forest classification and ranking the importance of DEGs, we identified membrane associated guanylate kinase, WW and PDZ domain containing 3 (MAGI3) as an important gene in CRC recurrence. …”
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“…RESULTS: Using publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. …”
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“…Three classification algorithms (logistical regression, random forest and AdaBoost) were evaluated for building predictive models. …”
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“…Significant associations among fecal microbial taxa, KO terms, and metabolites were observed, allowing for high-accuracy prediction of diet group by random forest analysis. CONCLUSIONS: Longitudinal sampling and a multi-modal approach to characterizing the gastrointestinal environment allowed us to demonstrate how drastically and quickly dietary changes impact the fecal microbiome and metabolite profiles of dogs following an abrupt dietary change and identify key microbe-metabolite relationships that allowed for treatment prediction. …”
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“…As a result, the yield and quality of Pear-jujube which constitute one of the dominant economic forests in this region, have been severely restricted. …”
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“…RESULTS: The results of our sentiment analysis showed that the bag-of-words tokenizing method using a random forest supervised learning approach produced the highest accuracy of the test set at 81.29%. …”
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“…The accumulation of the elements in the roots, stems and leaves of bilberry from four sites (in the nearest vicinity of a zinc smelter, a Mining and Metallurgical Plant, a main road with a high traffic volume and an unprotected natural forest community) were measured using optical emission spectrometry with excitation using inductively coupled argon plasma after wet acid digestion. …”
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“…We developed two random forest algorithms using the training data (80%) and tenfold cross validation to predict the CES-D-10 classes—1. …”
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“…Taxonomic profiles including reduced Parasutterella were associated with clinical disease progression over a mean follow-up of 3.7 years. Random forest classifiers using MLI bacterial abundances could distinguish disease state (area under the curve (AUC) 0.93), stricturing or penetrating Crohn’s disease behavior (AUC 0.82), and future clinical disease progression (AUC 0.74). …”
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“…METHODS: The sequencing data, as well as corresponding clinicopathological information of PC were collected from public databases. Random forest screening, least absolute shrinkage, and selection operator (LASSO) regression and multivariate Cox regression analysis were performed to construct a prognostic model. …”
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“…After applying feature selection, 8 and 10 features were found to positively predict next-day physiological and perceived stress, respectively. A random forest classifier performed the best in predicting next-day physiological stress (F1 score of 0.84) and next-day perceived stress (F1 score of 0.74) by using all features. …”
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“…Dynamic balance control was assessed while participants walked on a treadmill at a self-selected speed while wearing a VR headset that projected an immersive forest scene. Visual conditions consisted of (1) no visual manipulations (speed-matched anterior/posterior optical flow), (2) 0.175 m mediolateral translational oscillations of the scene that consisted of low pairing (0.1 and 0.31 Hz) or (3) high pairing (0.15 and 0.465 Hz) frequencies, (4) 5 degree medial–lateral rotational oscillations of virtual trees at a low frequency pairing (0.1 and 0.31 Hz), and (5) a combination of the tree and scene movements in (3) and (4). …”
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“…Military personnel and forest workers and miners were 1,154 times (AOR = 197.03; 95% CI 145.93, 9,131.56) and 44 times (AOR = 44.16; 95% CI 4.08, 477,93) more likely to be imported cases as compared to those working as employees and traders. …”
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“…Metabolite enriched pathways, random forest (RF), support vector machines (SVM) and multilayer perceptron neural network (MLP) were performed using Metaboanalyst 5.0, “caret” R package, “e1071” R package and “Tensorflow” Python package, respectively. …”
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