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Random Forests (RF) are at the cutting edge of supervised machine learning in terms of prediction performance, especially in genomics.
Tripping the switch fantastic: How a protein kinase cascade can convert graded inputs into switch-like outputs
James E. Ferrell Jr · 1996
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Statistical learning theory, 1998
Vladimir Vapnik · 1998
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Robustness of a gene regulatory circuit
J. W. Little, Shepley, and Wert · 1999
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Mining frequent patterns without candidate generation
Jiawei Han, Jian Pei, and Yiwen Yin · 2000
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Random forests
Leo Breiman · 2001
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Greedy function approximation: A gradient boosting machine
Jerome H Friedman · 2001
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Making best use of model evaluations to compute sensitivity indices
Andrea Saltelli · 2002
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Consistency for a simple model of random forests
Leo Breiman · 2004
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Quantitative kinetic analysis of the bacteriophage genetic network
O. Kobiler, A. Rokney, N. Friedman, D. L. Court, J. Stavans, and A. B. Oppenheim · 2005
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Threshold effects in gene regulation: When some is not enough
J. W. Little · 2005
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On model selection consistency of lasso
Peng Zhao and Bin Yu · 2006
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Variable importance in binary regression trees and forests
Hemant Ishwaran · 2007
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Mipred: classification of real and pseudo microrna precursors using random forest prediction model with combined features
Peng Jiang, Haonan Wu, Wenkai Wang, Wei Ma, Xiao Sun, and Zuhong Lu · 2007
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Unbiased split selection for classification trees based on the gini index
Carolin Strobl, Anne-Laure Boulesteix, and Thomas Augustin · 2007
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Small rnas establish gene expression thresholds
Erel Levine and Terence Hwa · 2008
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Conditional variable importance for random forests
Carolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, and Achim Zeileis · 2008
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MegaSNPHunter: A learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study
Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson LS Tang, and Weichuan Yu · 2009
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SNPInterForest: a new method for detecting epistatic interactions
Makiko Yoshida and Asako Koike · 2011
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Analysis of a random forests model
Gérard Biau · 2012
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Random forests for genomic data analysis
Xi Chen and Hemant Ishwaran · 2012
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Formal Hypothesis Tests for Additive Structure in Random Forests
Lucas Mentch and Giles Hooker · 2017
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Iterative random forests to discover predictive and stable high-order interactions
Sumanta Basu, Karl Kumbier, James B. Brown, and Bin Yu · 2018
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A computationally fast variable importance test for random forests for high-dimensional data
Silke Janitza, Ender Celik, and Anne-Laure Boulesteix · 2018
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Refining interaction search through signed iterative random forests
Karl Kumbier, Sumanta Basu, James B. Brown, Susan Celniker, and Bin Yu · 2018
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The revival of the gini importance?
Stefano Nembrini, Inke R König, and Marvin N Wright · 2018
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Data mining in the life sciences with random forest: a walk in the park or lost in the jungle?
Wouter G. Touw, Jumamurat R. Bayjanov, Lex Overmars, Lennart Backus, Jos Boekhorst, Michiel Wels, and Sacha A. F. T. van Hijum · 2012
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Integrative annotation of chromatin elements from encode data
Michael M Hoffman, Jason Ernst, Steven P Wilder, Anshul Kundaje, Robert S Harris, Max Libbrecht, Belinda Giardine, Paul M Ellenbogen, Jeffrey A Bilmes, Ewan Birney, et al · 2013
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Understanding variable importances in forests of randomized trees
Gilles Louppe, Louis Wehenkel, Antonio Sutera, and Pierre Geurts · 2013
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Narrowing the gap: Random forests in theory and in practice
Misha Denil, David Matheson, and Nando De Freitas · 2014
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Random intersection trees
Rajen Dinesh Shah and Nicolai Meinshausen · 2014
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Statistical learning with sparsity
Trevor Hastie, Robert Tibshirani, and Martin Wainwright · 2015
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Stefan Wager and Susan Athey · 2018
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A high-performance computing implementation of iterative random forest for the creation of predictive expression networks
Ashley Cliff, Jonathon Romero, David Kainer, Angelica Walker, Anna Furches, and Daniel Jacobson · 2019
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A debiased mdi feature importance measure for random forests
Xiao Li, Yu Wang, Sumanta Basu, Karl Kumbier, and Bin Yu · 2019
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Bias in the intervention in prediction measure in random forests: Illustrations and recommendations
Stefano Nembrini · 2019
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Conditional permutation importance revisited
Dries Debeer and Carolin Strobl · 2020
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Unbiased variable importance for random forests
Markus Loecher · 2020
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Veridical data science
Bin Yu and Karl Kumbier · 2020
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Unbiased measurement of feature importance in tree-based methods
Zhengze Zhou and Giles Hooker · 2020
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SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Clément Bénard, Gérard Biau, Sébastien da Veiga, and Erwan Scornet · 2021
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SIRUS: Stable and Interpretable RUle Set for classification
Clément Bénard, Gérard Biau, Sébastien Da Veiga, and Erwan Scornet · 2021
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