Machine learning applied to transcriptomic data to identify genes associated with feed efficiency in pigs

@inproceedings{Piles2019MachineLA,
  title={Machine learning applied to transcriptomic data to identify genes associated with feed efficiency in pigs},
  author={Miriam Piles and Carlos Fernandez-Lozano and Mar{\'i}a Velasco-Galilea and Olga Gonz{\'a}lez-Rodr{\'i}guez and Juan P S{\'a}nchez and David Torrallardona and Maria Ballester and Raquel Quintanilla},
  booktitle={Genetics Selection Evolution},
  year={2019}
}
BackgroundTo date, the molecular mechanisms that underlie residual feed intake (RFI) in pigs are unknown. Results from different genome-wide association studies and gene expression analyses are not always consistent. The aim of this research was to use machine learning to identify genes associated with feed efficiency (FE) using transcriptomic (RNA-Seq) data from pigs that are phenotypically extreme for RFI.MethodsRFI was computed by considering within-sex regression on mean metabolic body… CONTINUE READING
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Investigation of muscle transcriptomes using gradient boosting machine learning identifies molecular predictors of feed efficiency in growing pigs

Farouk Messad, Isabelle Louveau, Basile Koffi, Hélène Gilbert, Florence Gondret
  • BMC Genomics
  • 2019
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