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Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions
BMC Bioinformatics volume 21, Article number: 495 (2020)
- The original article was published in BMC Bioinformatics 2020 21:374
Correction to: BMC Bioinformatics (2020) 21:374 https://doi.org/10.1186/s12859-020-03718-9
Following publication of the original article , 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.
Using trees to identify interactions dates back to  and partial dependence plots to examine candidate feature interactions. Some algorithms identify sets of conditional or sequential splits, while other strategies (i.e., ) 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)  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 .
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.
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
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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