EFS: an ensemble feature selection tool implemented as R-package and web-application.

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A fast approach to detect gene-gene synergy.diceR: an R package for class discovery using an ensemble driven approach.Diabetes classification model based on boosting algorithms.

P2860

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P2860

EFS: an ensemble feature selection tool implemented as R-package and web-application.

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2017 nî lūn-bûn

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EFS: an ensemble feature selection tool implemented as R-package and web-application.

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EFS: an ensemble feature selection tool implemented as R-package and web-application.

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EFS: an ensemble feature selection tool implemented as R-package and web-application.

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P1476

EFS: an ensemble feature selection tool implemented as R-package and web-application

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P2093

Nikita Genze

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Ursula Neumann

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P2860

Random Forests

Non-invasive separation of alcoholic and non-alcoholic liver disease with predictive modeling

Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach

ROCR: visualizing classifier performance in R

Conditional variable importance for random forests.

Structure of HIV-1 quasi-species as early indicator for switches of co-receptor tropism.

Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach.

Stable feature selection for biomarker discovery.

On the limits of computational functional genomics for bacterial lifestyle prediction.

Baseline activity predicts working memory load of preceding task condition.

P2888

s13040-017-0142-8

P304

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scholarly article

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10.1186/S13040-017-0142-8

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Dominik Heider

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2017-06-27T00:00:00Z

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P6179

1086285517

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28674556

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5488355

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EFS: an ensemble feature selection tool implemented as R-package and web-application.

about

P2860

P2860

EFS: an ensemble feature selection tool implemented as R-package and web-application.

description

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type

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P1433

P1476

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