“…Area 5 is a relatively little-visited place in the forest park, where animals are rarely walked. METHODS: Before sampling, vegetation cover and rubbish were removed from the soil area, then ∼ 100 g of soil was taken from the surface using aseptic rules in sterile plastic containers. …”
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“…The four machine learning algorithms include extreme gradient boosting (XGBoost), random forest (RF), light gradient boosting machine (LightGBM), and adaptive boosting (AdaBoost) which were used to build predictive models. …”
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“…Six different ML classification models were trained (logistic regression, least absolute shrinkage and selection operator [LASSO], ridge regression, decision tree, random forest, and gradient boosting machine). The primary outcome was to predict depression at 2 years following study enrollment. …”
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“…Further analysis with a random forest revealed that, in addition to chronological age and hearing age, simultaneous versus sequential implantation, communication mode at school, and social integration were predictors of cochlear implant users’ receptive vocabulary. …”
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“…Extracting these plants from the forest for commercial purposes and long maturation periods of close to 30 years have contributed to their loss. …”
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“…We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. Methods: Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. …”
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“…The heterogeneity and sensitivity were detected by Stata 14.0 software, and the correlation between Fn and cancer clinicopathological as the effect variables was assessed according to the odds ratio (OR) and 95% confidence interval (CI). The forest plot was drawn. RESULTS: A total of 19 articles meeting the inclusion criteria were selected. …”
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“…The biomarkers were obtained through random forest (RF) algorithm and repeated cross validation. …”
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“…The pooled prevalence of health-related quality of life of cancer with a 95% confidence interval was presented using forest plots. RESULTS: A total of 12 studies with 3, 479 participants were included in this review and the overall pooled estimates mean score of health-related quality of life among cancer patients in Ethiopia was 57.91(44.55, 71.27, I(2) = 98.8%, p≤0.001). …”
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“…Global declines in seagrass beds and kelp forests (linked to global warming) are predicted to cause substantial reductions in the amounts of marine organic matter reaching many beach ecosystems, likely causing flow‐on effects for food webs and biodiversity. …”
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“…We carried out a descriptive analysis and predicted the publication outcome using 2 approaches: a neural network with a large domain-specific language model and a random forest classifier using a weighted bag-of-words representation of text. …”
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“…Canopy community distribution confirms the results obtained in a previous study which suggest the stability of native and endemic arthropods species communities over introduced species community in native forests fragments. Arthropod species were richer than bryophytes, lichens and vascular plants species. …”
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“…Altogether, our null model approach allowed us to detect patterns of environmental filtering, which differed between traits and can be applied in the future to understand community assembly changes in Chinese hilly forest ecosystems.…”
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“…Hence, by focusing on the relationship between PSMC genes and the prognostic, genomic, and tumor microenvironment features in lung adenocarcinoma (LUAD), a PSMC-based prognostic signature was established using consensus clustering and multiple machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO) Cox regression, CoxBoost, and survival random forest analysis in TCGA and GSE72094. We then validated it in three independent cohorts from GEO and estimated the correlation between risk score and clinical features: genomic features (alterations, tumor mutation burden, and copy number variants), immune profiles (immune score, TIDE score, tumor-infiltrated immune cells, and immune checkpoints), sensitivity to chemotherapy (GDSC, GSE42127, and GSE14814), and immunotherapy (IMvigor210, GSE63557, and immunophenoscore). …”
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“…METHODS: In total, 6 focus groups were conducted at Wake Forest Baptist Health (WFBH) between September 2019 and February 2020. …”
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“…With 395 potential predictors (age, diagnosis, and drug prescription records from EHR), 10-year sex-specific hip fracture prediction models were developed using stepwise selection by logistic regression (LR) and four machine learning (ML) algorithms (gradient boosting machine, random forest, eXtreme gradient boosting, and single-layer neural networks) in the training cohort. …”
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“…According to machine learning analysis, the top 5 algorithms were XGBoost regression (area under the receiver operating characteristic curve of 99%), XGBoost classification (area under the receiver operating characteristic curve of 98%), LightGBM (area under the receiver operating characteristic curve of 94%), random forest regression (area under the receiver operating characteristic curve of 86%), and linear regression (area under the receiver operating characteristic curve of 78%). …”
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“…Then, we used the Node2Vec embedding method to encode SNOMED-CT (Systematized Nomenclature of Medicine-Clinical Terms) medical codes and applied random forest regression to estimate causal effects. We used both methods to estimate causal effects of antidepressants on COVID-19 outcomes. …”
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