TY - JOUR
T1 - Machine learning, the kidney, and genotype–phenotype analysis
AU - Sealfon, Rachel S.G.
AU - Mariani, Laura H.
AU - Kretzler, Matthias
AU - Troyanskaya, Olga G.
N1 - Funding Information:
The authors thank Aaron K. Wong for feedback on the manuscript. LHM is supported by the National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases ( K08 DK115891 ).
Funding Information:
The authors thank Aaron K. Wong for feedback on the manuscript. LHM is supported by the National Institutes of Health (NIH)/National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (K08 DK115891). This work is supported by NIH/NIDDK grants U24DK100845, UGDK114907, and U2CDK114886 and NIH grant UH3TR002158 to OGT. OGT is a senior fellow of the Genetic Networks program of the Canadian Institute for Advanced Research (CIFAR).
Publisher Copyright:
© 2020 International Society of Nephrology
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
KW - deep learning
KW - genotype
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85083876134&partnerID=8YFLogxK
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U2 - 10.1016/j.kint.2020.02.028
DO - 10.1016/j.kint.2020.02.028
M3 - Review article
C2 - 32359808
AN - SCOPUS:85083876134
SN - 0085-2538
VL - 97
SP - 1141
EP - 1149
JO - Kidney International
JF - Kidney International
IS - 6
ER -