“…Study incidence heterogeneity rates were assessed using x (2) heterogeneity statistics, graphically represented with Forest plots. RESULTS: We identified 19 studies meeting the inclusion criteria (all published after 1999). 18 studies reported upon the incidence of persistent/chronic diarrhea as a syndromic diagnosis in returning travelers; one study reported adequate denominator data from which to assess pathogen specific etiology. …”
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“…METHODS: We investigated the floral biology, pollinator assemblages, breeding system and nectar production patterns ofP. gentianoides inhabiting a temperate montane forest in central Mexico. Pollination experiments were also conducted to assess the pollinator effectiveness of bumblebees and hummingbirds. …”
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“…Epigenetic memory in Norway spruce affects the timing of bud burst and bud set, vitally important adaptive traits for this long-lived forest species. Epigenetic memory is established in response to the temperature conditions during embryogenesis. …”
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“…The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). …”
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“…It is reasonable to use these over other antibiotics when appropriate (community acquired pneumonia, bronchitis, chlamydia, rickettsial or spirochetal infections) to reduce the risk of CDI. Forest plot demonstrating decreased odds of CDI with tetracyclines use by a random-effects model DISCLOSURES: All authors: No reported disclosures.…”
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“…RESULTS: The overall prevalence of Lipoptena cervi infection with Bartonella spp. was 75.12% (163/217); 86.67% (104/120) of females and 60.82% (59/97) of males collected from red deer hunted in the Strzałowo Forest District in Poland (53°45′57.03″N, 21°25′17.79″E) were infected. …”
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“…Diagnostically relevant alterations, such as 1p/19q codeletion, and focal amplifications could be recapitulated. Using ad hoc random forests, we could perform supervised pan-cancer classification to distinguish gliomas, medulloblastomas, and brain metastases of different primary sites. …”
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“…This enhances the sensitivity of the test and by using a random forest classifier approach, where the UroMark results are derived from a cut off generated from known outcomes of previous samples, addresses many shortcomings of previous assays. …”
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“…Univariate analysis, logistic regression, and random forest (RF) algorithms were used for data analysis in R. …”
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“…RR-BLUP, Bayesian A, B, Cπ, LASSO, Ridge Regression and two machine learning methods (SVM and Random Forest) were used to evaluate GS accuracy of the traits. …”
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“…The SDM were conducted using two algorithms for rare species, MaxEnt and randomForest (RF), and were based on seven localities for Rhagomys, and 10 for Neusticomys. …”
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“…Using the features obtained from both the BEST search engine scores and word vectors, we extract mutation-gene and mutation-drug relations from the literature using machine learning classifiers such as random forest and deep convolutional neural networks. Our methods achieved better results compared with the state-of-the-art methods. …”
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“…We therefore investigated the role of facial cues for species recognition in wild red-fronted lemurs (Eulemur rufifrons) at Kirindy Forest. We presented adult red-fronted lemurs pictures of male faces from five species including red-fronted lemurs, three closely related species, white-fronted lemurs (E. albifrons), brown lemurs (E. fulvus), rufous brown lemurs (E. rufus), and genetically more distant red-bellied lemurs (E. rubriventer), occurring in allopatry with the study population. …”
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“…The occurrence data were supplemented with pseudo-absence data as input to Random Forests. The resulting suitability and maximum risk of establishment maps were combined into hard-classified maps per country for expert validation. …”
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“…To address this complex problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using random forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 15% of the variance reducing the root mean squared error (RMSE) from 0.96 log(10) to 0.85 log(10) mg/kg/day, providing a baseline performance metric (lower expectation of model performance). …”
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“…The continuous process of urbanization and expansion of human activities in forest areas impacts natural habitats, modifying the ecology of some species of Leishmania, as well as its vectors and reservoirs and, consequently, changes the epidemiological pattern that contributes to the expansion of American cutaneous leishmaniasis in Brazil. …”
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“…RESULTS: Individual stochastic gradient boosting and random forest classifiers trained on only empirically validated long non-protein coding RNAs were constructed. …”
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“…BACKGROUND: The mangrove crab Ucides cordatus is considered a key species for the ecological balance of mangrove forests and a major source of employment and income for traditional crab collectors in Brazil. …”
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“…RESULTS: The Spark Random Forest and Decision Trees with oversampling and undersampling techniques, and built with only alignment-based similarity measures or combined with several alignment-free pairwise protein features showed the highest classification performance for ortholog detection in three yeast proteome pairs. …”
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“…Any one of the features can predict disease status of the training samples with the average of sensitivity and specificity higher than 0.8. The random forests classification using the top 10 group-specific features yielded a higher AUC (from ∼0.8 to ∼0.9) than that of previous studies. …”
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