Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics
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GPURFSCREEN: a GPU based virtual screening tool using random forest classifierDifferent Statistical Approaches to Investigate Porcine Muscle Metabolome Profiles to Highlight New Biomarkers for Pork Quality AssessmentModernizing Relationship Therapy through Social Thermoregulation Theory: Evidence, Hypotheses, and Explorations.The differential effects of increasing frequency and magnitude of extreme events on coral populations.Comparative analyses between retained introns and constitutively spliced introns in Arabidopsis thaliana using random forest and support vector machine.Application of data mining methods for classification and prediction of olive oil blends with other vegetable oils.RAD sequencing reveals within-generation polygenic selection in response to anthropogenic organic and metal contamination in North Atlantic Eels.Do little interactions get lost in dark random forests?Intervention in prediction measure: a new approach to assessing variable importance for random forests.Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach.Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.Letter to the Editor: On the term 'interaction' and related phrases in the literature on Random Forests.Using random forests for assistance in the curation of G-protein coupled receptor databases.Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI.[Formula: see text] splitting rules in survival forests.EFS: an ensemble feature selection tool implemented as R-package and web-application.Local Chromatin Features Including PU.1 and IKAROS Binding and H3K4 Methylation Shape the Repertoire of Immunoglobulin Kappa Genes Chosen for V(D)J Recombination.Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system.Stripping flow cytometry: How many detectors do we need for bacterial identification?Applications of random forest feature selection for fine-scale genetic population assignment.An application of machine learning to haematological diagnosis.Using cell nuclei features to detect colon cancer tissue in hematoxylin and eosin stained slides.Textural analysis of early-phase spatiotemporal changes in contrast enhancement of breast lesions imaged with an ultrafast DCE-MRI protocol.Identifying cryptic diversity with predictive phylogeography.Mining data with random forests: current options for real-world applications
P2860
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P2860
Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics
description
wetenschappelijk artikel
@nl
наукова стаття, опублікована в жовтні 2012
@uk
name
Overview of random forest meth ...... nal biology and bioinformatics
@en
Overview of random forest meth ...... nal biology and bioinformatics
@nl
type
label
Overview of random forest meth ...... nal biology and bioinformatics
@en
Overview of random forest meth ...... nal biology and bioinformatics
@nl
prefLabel
Overview of random forest meth ...... nal biology and bioinformatics
@en
Overview of random forest meth ...... nal biology and bioinformatics
@nl
P2860
P356
P1476
Overview of random forest meth ...... nal biology and bioinformatics
@en
P2093
Inke R. König
Jochen Kruppa
P2860
P304
P356
10.1002/WIDM.1072
P577
2012-10-18T00:00:00Z