Machine Learning
Microarray data mining with visual programming
Bioinformatics
Microarray-based classification and clinical predictors
Bioinformatics
Bioinformatics
Patient-centered yes/no prognosis using learning machines
International Journal of Data Mining and Bioinformatics
Predictor correlation impacts machine learning algorithms
Bioinformatics
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Maximal conditional chi-square importance in random forests
Bioinformatics
Bioinformatics
Bioinformatics
Variable selection using random forests
Pattern Recognition Letters
Mining data with random forests: A survey and results of new tests
Pattern Recognition
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The random forest (RF) algorithm by Leo Breiman has become a standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with complex interaction structures as well as highly correlated variables and return measures of variable importance. This paper synthesizes 10 years of RF development with emphasis on applications to bioinformatics and computational biology. Special attention is paid to practical aspects such as the selection of parameters, available RF implementations, and important pitfalls and biases of RF and its variable importance measures (VIMs). The paper surveys recent developments of the methodology relevant to bioinformatics as well as some representative examples of RF applications in this context and possible directions for future research. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.