“…With a 2000:271,132 (true sites:false sites) training set, χ(2)-DT achieves the highest independent test accuracy (93.34%) when compared with three classifiers (random forest, artificial neural network, and relaxed variable kernel density estimator) and takes a short computation time (89 s). χ(2)-DT also exhibits good independent test accuracy (92.40%), when validated with BG-570 mutated sequences with frameshift errors (nucleotide insertions and deletions). …”
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“…Quantitative analysis was performed by generating forest plots and calculating effect sizes by random-effect models. …”
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“…The frequency of 1532T was highest (0.537) in the population from the Olympic Forest Park (OFP, Chao Yang District), but not detectable in Huai Rou and Mi Yun. …”
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“…In the training set (70% random sample), using routinely available triage data as predictors (eg, demographic characteristics and vital signs), we derived 4 machine learning–based models: lasso regression, random forest, gradient-boosted decision tree, and deep neural network. …”
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“…Various lifestyle parameters were measured, including walking steps, conversation time, total sleep time (TST), sleep efficiency, time awake after sleep onset, awakening count, napping time, and heart rate. Random forest (RF) regression analysis was used to examine the relationships between total daily sensing data and Mini-Mental State Examination (MMSE) scores. …”
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“…BACKGROUND: Formerly known as the Malaysian hunter gatherers, the Negrito Orang Asli (OA) were heavily dependent on the forest for sustenance and early studies indicated high prevalence of intestinal parasitism. …”
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“…METHODS AND FINDINGS: The study was implemented between April 2009 and December 2011 in four neighboring villages in a remote forested area of Quang Nam province. P. vivax-infected patients were treated radically with chloroquine (CQ; 25 mg/kg over 3 days) and primaquine (PQ; 0.5 mg/kg/day for 10 days) and visited monthly (malaria symptoms and blood sampling) for up to 2 years. …”
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“…Comparative genomics also contributed to enlightening fungal decay mechanisms in conversion and cycling of recalcitrant organic carbon in the forest ecosystems. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-019-5817-8) contains supplementary material, which is available to authorized users.…”
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“…To compare the predictivity of the feature spaces for the toxicity endpoint, we next trained Random Forest (RF) acute oral toxicity classifiers on either molecular, protein target and qHTS descriptors. …”
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“…After selecting a subset of 31 candidate variables from the literature, we constructed prediction models by five widely-used machine learning classifiers: neural network (multilayer perceptron: MLP), support vector machine (SVM), random forest (RF), k-nearest neighbor (K-NN) and logistic regression (LR). …”
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“…A framework was developed that combines six machine learning algorithms, including artificial neural network (ANN), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), random forest (RF), Gaussian process (GP), and PLSR to optimize high-throughput analysis of the two photosynthetic variables. …”
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“…During function-based screening of previously generated forest soil metagenomic libraries for Escherichia coli clones conferring phytase activity, two positive clones harboring the plasmids pLP05 and pLP12 were detected. …”
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“…Results were presented in forest plot, tables and figures with 95% CI. The Cochrane Q test and I(2) test statistic were used to test heterogeneity across studies. …”
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“…To assess the effect of social norms on the behaviour of health workers, we will perform fixed effects meta-analysis and present forest plots, stratified by behaviour change technique. …”
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“…These results were also corroborated by the variable importance given to the “species” during the internal cross validation (CV) run with Random Forest (RF). CONCLUSIONS: The unsupervised analysis (PCA and two-way heatmaps) of the log2-cpm normalized data and relative abundance differential analysis seemed to suggest that the bacterial signature of common “species” was distinctive across the cities; which was also supported by the variable importance results. …”
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“…Since 2015 (125 studies [48.4%]), the most common methods were support vector machines (37 studies [29.6%]) and random forests (29 [23.2%]). CONCLUSIONS: The rate of publication of studies using machine learning to analyse routinely collected ICU data is increasing rapidly. …”
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“…Retrospective analysis of all urine microscopy, culture, and sensitivity reports over one year was used to compare two methods of classification: a heuristic model using a combination of white blood cell count and bacterial count, and a machine learning approach testing three algorithms (Random Forest, Neural Network, Extreme Gradient Boosting) whilst factoring in independent variables including demographics, historical urine culture results, and clinical details provided with the specimen. …”
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“…Expression levels of intratumoral and stromal immune markers were compared in relation to survival using Kaplan-Meier curves, random survival forest model and survival tree analysis. A multivariable Cox proportional-hazards model of associated markers was used to calculate the risk scores. …”
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“…For instance, Phytophthora ramorum, Phytophthora cryptogea, Phytophthora plurivora and Fusarium solani cause significant losses in nurseries and in forest ecosystems. Chemical treatments, while harmful to the environment and human health, have been proved to have little or no impact on these species. …”
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