Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions (BMC Bioinformatics, (2020), 21, 1, (374), 10.1186/s12859-020-03718-9)

Samir Rachid Zaim, Colleen Kenost, Joanne Berghout, Wesley Chiu, Liam Wilson, Hao Helen Zhang, Yves A. Lussier

Research output: Contribution to journalComment/debatepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number495
JournalBMC bioinformatics
Volume21
Issue number1
DOIs
StatePublished - Dec 1 2020

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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