Machine learning, the kidney, and genotype–phenotype analysis

Rachel S.G. Sealfon, Laura H. Mariani, Matthias Kretzler, Olga G. Troyanskaya

Research output: Contribution to journalReview articlepeer-review

22 Scopus citations


With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale biological data sets. The increasing availability of comprehensive kidney omics compendia (transcriptomics, proteomics, metabolomics, and genome sequencing), as well as other data modalities such as electronic health records, digital nephropathology repositories, and radiology renal images, makes machine learning approaches increasingly essential for analyzing human kidney data sets. Here, we discuss how machine learning approaches can be applied to the study of kidney disease, with a particular focus on how they can be used for understanding the relationship between genotype and phenotype.

Original languageEnglish (US)
Pages (from-to)1141-1149
Number of pages9
JournalKidney International
Issue number6
StatePublished - Jun 2020

All Science Journal Classification (ASJC) codes

  • Nephrology


  • deep learning
  • genotype
  • machine learning


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