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binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions.

Samir Rachid ZaimColleen KenostJoanne BerghoutWesley ChiuLiam WilsonHao Helen ZhangYves A Lussier
Published in: BMC bioinformatics (2020)
binomialRF extends upon previous methods for identifying interpretable features in RFs and brings them together under a correlated binomial distribution to create an efficient hypothesis testing algorithm that identifies biomarkers' main effects and interactions. Preliminary results in simulations demonstrate computational gains while retaining competitive model selection and classification accuracies. Future work will extend this framework to incorporate ontologies that provide pathway-level feature selection from gene expression input data.
Keyphrases
  • machine learning
  • deep learning
  • gene expression
  • big data
  • climate change
  • dna methylation
  • neural network
  • artificial intelligence
  • genome wide
  • electronic health record
  • molecular dynamics
  • current status
  • monte carlo