Random Forest for Bioinformatics



Modern biology has experienced an increased use of machine learning techniques for large scale and complex biological data analysis. In the area of Bioinformatics, the Random Forest (RF) [6] technique, which includes an ensemble of decision trees and incorporates feature selection and interactions naturally in the learning process, is a popular choice. It is nonparametric, interpretable, efficient, and has high prediction accuracy for many types of data. Recent work in computational biology has seen an increased use of RF, owing to its unique advantages in dealing with small sample size, high-dimensional feature space, and complex data structures.


Feature Selection Random Forest Gini Index Importance Measure Feature Importance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Machine Learning DepartmentNEC Labs AmericaPrincetonUSA
  2. 2.PrincetonUSA

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