Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions
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CORRECTION
Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions Samir Rachid Zaim1,2,3, Colleen Kenost1,3, Joanne Berghout1,3, Wesley Chiu1,3, Liam Wilson1,3, Hao Helen Zhang1,2,4* and Yves A. Lussier1,2,3,5,6,7* The original article can be found online at https://doi. org/10.1186/s12859-02003718-9. *Correspondence: [email protected]; [email protected] 1 Center for Biomedical Informatics and Biostatistics, University of Arizona Health Sciences, 1230 N. Cherry Ave, Tucson, AZ 85721, USA 7 The University of Arizona BIO5 Institute, 1657 E. Helen Street, Tucson, AZ 85721, USA Full list of author information is available at the end of the article
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.
Author details 1 Center for Biomedical Informatics and Biostatistics, University of Arizona Health Sciences, 1230 N. Cherry Ave, Tucson, AZ 85721, USA. 2 The Graduate Interdisciplinary Program in Statistics, The University of Arizona, 617 N. Santa Rita Ave, Tucson, AZ 85721, USA. 3 College of Medicine, Tucson, 1501 N. Campbell Ave, Tucson, AZ 85721, USA. 4 Department of Mathematics, College of Sciences, The University of Arizona, 617 N. Santa Rita Ave, Tucson, AZ 85721, USA. 5 The Center for Applied Genetic and Genomic Medicine, 1295 N. Martin, Tucson, AZ 85721, USA. 6 The University of Arizona Cancer
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