Correction to: BMC Bioinformatics (2020) 21:374 https://doi.org/10.1186/s12859-020-03718-9
Following publication of the original article [1], errors were identified in the References of the Discussion section.
The updated discussion is given below and the changes have been highlighted in bold typeface.
Discussion
Using trees to identify interactions dates back to [37] and partial dependence plots to examine candidate feature interactions. Some algorithms identify sets of conditional or sequential splits, while other strategies (i.e., [37]) measure their effect in prediction error. More recently, works such as [25, 58] look at the frequency of sequence of splits or "decision paths" as a way to determine whether two features interact in the tree-splitting process. For example, iterative random forests (iRF) [58] identify decision paths along random forests and captures their prevalence, therefore benefitting from a combinatoric feature space reduction in the interaction search. Similarly, BART conducts interaction screening by looking at inclusion frequencies of pairs of predictors [25].
-
58.
Basu, Sumanta, Karl Kumbier, James B. Brown, and Bin Yu. Iterative random forests to discover predictive and stable high-order interactions. Proceedings of the National Academy of Sciences 115, no. 8 (2018): 1943–1948.
Reference
Zaim R et al. binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions. BMC Bioinform. 2020;21:374. https://doi.org/10.1186/s12859-020-03718-9
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Zaim, S.R., Kenost, C., Berghout, J. et al. Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions. BMC Bioinformatics 21, 495 (2020). https://doi.org/10.1186/s12859-020-03822-w
Published:
DOI: https://doi.org/10.1186/s12859-020-03822-w