Journal
KIDNEY INTERNATIONAL
Volume 97, Issue 6, Pages 1141-1149Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.kint.2020.02.028
Keywords
deep learning; genotype; machine learning
Categories
Funding
- National Institutes of Health (NIH)/National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) [K08 DK115891]
- NIH/NIDDK [U24DK100845, UGDK114907, U2CDK114886]
- NIH [UH3TR002158]
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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.
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