Adapting Random Forests to Predict Obesity-Associated Gene Expression

Jeremy Watts, Elexis Allen, Ahmad Mitoubsi, Anahita Khojandi, James Eales, Farideh Jalali-Najafabadi, Theodore Papamarkou

Research output: Contribution to conferencePaperpeer-review

Abstract

Random forests (RFs) are effective at predicting gene expression from genotype data. However, a comparison of RF regressors and classifiers, including feature selection and encoding, has been under-explored in the context of gene expression prediction. Specifically, we examine the role of ordinal or one-hot encoding and of data balancing via oversam-pling in the prediction of obesity-associated gene expression. Our work shows that RFs compete with PrediXcan in the prediction of obesity-associated gene expression in subcutaneous adipose tissue, a highly relevant tissue to obesity. Additionally, RFs generate predictions for obesity-associated genes where PrediXcan fails to do so.

Original languageEnglish
Pages4407-4410
Number of pages4
DOIs
Publication statusPublished - 8 Sept 2022
Event44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) -
Duration: 11 Jul 202215 Jul 2022

Conference

Conference44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Period11/07/2215/07/22

Keywords

  • Algorithms
  • Gene Expression
  • Humans
  • Obesity/genetics

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