title: CloudForest: A Scalable and Efficient Random Forest Implementation for Biological Data.
abstract: Random Forest has become a standard data analysis tool in computational biology. However, extensions to existing implementations are often necessary to handle the complexity of biological datasets and their associated research questions. The growing size of these datasets requires high performance implementations. We describe CloudForest, a Random Forest package written in Go, which is particularly well suited for large, heterogeneous, genetic and biomedical datasets. CloudForest includes several extensions, such as dealing with unbalanced classes and missing values. Its flexible design enables users to easily implement additional extensions. CloudForest achieves fast running times by effective use of the CPU cache, optimizing for different classes of features and efficiently multi-threading. https://github.com/ilyalab/CloudForest.
authors.affiliation: Institute for Systems Biology, Seattle, WA, United States of America.
authors.forename: Ryan
authors.initials: R
authors.lastname: Bressler
authors.affiliation: Institute for Systems Biology, Seattle, WA, United States of America.
authors.forename: Richard B
authors.initials: RB
authors.lastname: Kreisberg
authors.affiliation: Institute for Systems Biology, Seattle, WA, United States of America.
authors.forename: Brady
authors.initials: B
authors.lastname: Bernard
authors.affiliation: Inova Translational Medicine Institute, Inova Health System and Inova Fairfax Medical Center, Falls Church, VA, United States of America.
authors.forename: John E
authors.initials: JE
authors.lastname: Niederhuber
authors.affiliation: Inova Translational Medicine Institute, Inova Health System and Inova Fairfax Medical Center, Falls Church, VA, United States of America.|Virginia Commonwealth University, School of Medicine, Richmond, VA, United States of America.
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Vockley
authors.affiliation: Institute for Systems Biology, Seattle, WA, United States of America.
authors.forename: Ilya
authors.initials: I
authors.lastname: Shmulevich
authors.affiliation: Institute for Systems Biology, Seattle, WA, United States of America.
authors.forename: Theo A
authors.initials: TA
authors.lastname: Knijnenburg
mesh_terms: D002965:Classification; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D011381:Programming Languages; D012044:Regression Analysis; D012984:Software