Machine learning, the kidney, and genotype-phenotype analysis.

TitleMachine learning, the kidney, and genotype-phenotype analysis.
Publication TypeJournal Article
Year of Publication2020
AuthorsSealfon, RSG, Mariani, LH, Kretzler, M, Troyanskaya, OG
JournalKidney Int
Volume97
Issue6
Pagination1141-1149
Date Published2020 06
ISSN1523-1755
Abstract

<p>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.</p>

DOI10.1016/j.kint.2020.02.028
Alternate JournalKidney Int
PubMed ID32359808
Grant ListK08 DK115891 / DK / NIDDK NIH HHS / United States
U24 DK100845 / DK / NIDDK NIH HHS / United States
UG3 DK114907 / DK / NIDDK NIH HHS / United States
U2C DK114886 / DK / NIDDK NIH HHS / United States
UH3 TR002158 / TR / NCATS NIH HHS / United States