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Data from: Automatic recognition of self-acknowledged limitations in clinical research literature
负责人:
关键词:
self-acknowledged limitations;clinical research literature;Natural Language Processing;research transparency
DOI:
doi:10.5061/dryad.06ds7
摘要:
the training set in order to improve classification performance. The machine learning algorithms used were logistic regression (LR) and support vector machines
Data from: A systems toxicology approach for the prediction of kidney toxicity and its mechanisms in vitro
负责人:
关键词:
Kidney toxicity;In Vitro and Alternatives;Prediction;mechanisms;Systems Toxicology
DOI:
doi:10.5061/dryad.646v2r1
摘要:
. Here, we used primary human kidney cells and applied a systems biology approach that combines multidimensional datasets and machine learning to identify
Data from: Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping
负责人:
关键词:
Environmental matrix;prediction mapping;Machine Learning
DOI:
doi:10.5061/dryad.1m8tg17
摘要:
relationship) to evaluate the performance of four machine learning (ML) classifiers (classification trees, random forests, support vector machines
Data from: Mie scattering and microparticle based characterization of heavy metal ions and classification by statistical inference methods
负责人:
关键词:
support vector machines;polystyrene;statistical classification;heavy metal;light scattering
DOI:
doi:10.5061/dryad.62n8p0q
摘要:
and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including
Data from: Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators
负责人:
关键词:
Learning systems;electrograms;Implantable cardioverter defibrillator;regionalization;Ventricular tachycardia;Localization;Spatial resolution;Machine Learning
DOI:
doi:10.5061/dryad.nm0v0
摘要:
ng the anatomical location of the Left Ventricular Tachycardia exit site (LVTES). Our aim here was to evaluate the possibilities from a machine learning
Data from: Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis thaliana
负责人:
关键词:
Systems biology;Arabidopsis thaliana;abiotic\/environmental stress;differential network;bioinformatics;transcriptome analysis;gene coexpression network;Random Forest;Machine Learning
DOI:
doi:10.5061/dryad.41b9g
摘要:
Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recogni
Data from: Development of machine learning models for diagnosis of glaucoma
负责人:
关键词:
cornea thickness;RNFL;glaucoma;ocular pressure
DOI:
doi:10.5061/dryad.q6ft5
摘要:
The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based
Data from: I meant to do that: determining the intentions of action in the face of disturbances
负责人:
关键词:
human;Feedback;feedforward;control of movement;Prediction;internal model;generative model;arm reaching;error;upper extremity;intention
DOI:
doi:10.5061/dryad.1257p
摘要:
the target. Knowing such an intent signal is broadly applicable: enhanced human-machine interaction, the study of impaired intent in neural disorders, the rea
Data from: Integrating life history traits into predictive phylogeography
负责人:
关键词:
cryptic diversity;comparative phylogeography;Machine Learning;Random Forest
DOI:
doi:10.5061/dryad.s6v210k
摘要:
about unsampled taxa using machine-learning techniques such as Random Forests. To date, organismal trait data have infrequently been incorporated into predictive
Data from: Biogeographic and anthropogenic correlates of Aleutian Islands plant diversity: a machine-learning approach
负责人:
关键词:
Anthropocene;Aleutian Islands Alaska;Patterns and Processes;Island Biogeography;Data Cloning;vascular plants;Machine Learning
DOI:
doi:10.5061/dryad.r12cq4r
摘要:
ng machine learning, stochastic boosting- TreeNet,) based on classic and Aleutians-specific island biogeography hypotheses. Plant species richness is strongl

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